938 results on '"Mishra, Gaurav"'
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2. Training Language Models on the Knowledge Graph: Insights on Hallucinations and Their Detectability
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Hron, Jiri, Culp, Laura, Elsayed, Gamaleldin, Liu, Rosanne, Adlam, Ben, Bileschi, Maxwell, Bohnet, Bernd, Co-Reyes, JD, Fiedel, Noah, Freeman, C. Daniel, Gur, Izzeddin, Kenealy, Kathleen, Lee, Jaehoon, Liu, Peter J., Mishra, Gaurav, Mordatch, Igor, Nova, Azade, Novak, Roman, Parisi, Aaron, Pennington, Jeffrey, Rizkowsky, Alex, Simpson, Isabelle, Sedghi, Hanie, Sohl-dickstein, Jascha, Swersky, Kevin, Vikram, Sharad, Warkentin, Tris, Xiao, Lechao, Xu, Kelvin, Snoek, Jasper, and Kornblith, Simon
- Subjects
Computer Science - Computation and Language ,Computer Science - Artificial Intelligence ,Computer Science - Machine Learning - Abstract
While many capabilities of language models (LMs) improve with increased training budget, the influence of scale on hallucinations is not yet fully understood. Hallucinations come in many forms, and there is no universally accepted definition. We thus focus on studying only those hallucinations where a correct answer appears verbatim in the training set. To fully control the training data content, we construct a knowledge graph (KG)-based dataset, and use it to train a set of increasingly large LMs. We find that for a fixed dataset, larger and longer-trained LMs hallucinate less. However, hallucinating on $\leq5$% of the training data requires an order of magnitude larger model, and thus an order of magnitude more compute, than Hoffmann et al. (2022) reported was optimal. Given this costliness, we study how hallucination detectors depend on scale. While we see detector size improves performance on fixed LM's outputs, we find an inverse relationship between the scale of the LM and the detectability of its hallucinations., Comment: Published at COLM 2024. 16 pages, 11 figures
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- 2024
3. An alcoholic person: Forensic psychological perspective
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Gupta, Shubham and Mishra, Gaurav
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- 2020
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4. Health-Hygiene practices found among kharwar tribals of Deoria District of Uttar Pradesh
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Mishra, Gaurav and Singh, Udai Pratap
- Published
- 2018
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5. Gemini: A Family of Highly Capable Multimodal Models
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Gemini Team, Anil, Rohan, Borgeaud, Sebastian, Alayrac, Jean-Baptiste, Yu, Jiahui, Soricut, Radu, Schalkwyk, Johan, Dai, Andrew M., Hauth, Anja, Millican, Katie, Silver, David, Johnson, Melvin, Antonoglou, Ioannis, Schrittwieser, Julian, Glaese, Amelia, Chen, Jilin, Pitler, Emily, Lillicrap, Timothy, Lazaridou, Angeliki, Firat, Orhan, Molloy, James, Isard, Michael, Barham, Paul R., Hennigan, Tom, Lee, Benjamin, Viola, Fabio, Reynolds, Malcolm, Xu, Yuanzhong, Doherty, Ryan, Collins, Eli, Meyer, Clemens, Rutherford, Eliza, Moreira, Erica, Ayoub, Kareem, Goel, Megha, Krawczyk, Jack, Du, Cosmo, Chi, Ed, Cheng, Heng-Tze, Ni, Eric, Shah, Purvi, Kane, Patrick, Chan, Betty, Faruqui, Manaal, Severyn, Aliaksei, Lin, Hanzhao, Li, YaGuang, Cheng, Yong, Ittycheriah, Abe, Mahdieh, Mahdis, Chen, Mia, Sun, Pei, Tran, Dustin, Bagri, Sumit, Lakshminarayanan, Balaji, Liu, Jeremiah, Orban, Andras, Güra, Fabian, Zhou, Hao, Song, Xinying, Boffy, Aurelien, Ganapathy, Harish, Zheng, Steven, Choe, HyunJeong, Weisz, Ágoston, Zhu, Tao, Lu, Yifeng, Gopal, Siddharth, Kahn, Jarrod, Kula, Maciej, Pitman, Jeff, Shah, Rushin, Taropa, Emanuel, Merey, Majd Al, Baeuml, Martin, Chen, Zhifeng, Shafey, Laurent El, Zhang, Yujing, Sercinoglu, Olcan, Tucker, George, Piqueras, Enrique, Krikun, Maxim, Barr, Iain, Savinov, Nikolay, Danihelka, Ivo, Roelofs, Becca, White, Anaïs, Andreassen, Anders, von Glehn, Tamara, Yagati, Lakshman, Kazemi, Mehran, Gonzalez, Lucas, Khalman, Misha, Sygnowski, Jakub, Frechette, Alexandre, Smith, Charlotte, Culp, Laura, Proleev, Lev, Luan, Yi, Chen, Xi, Lottes, James, Schucher, Nathan, Lebron, Federico, Rrustemi, Alban, Clay, Natalie, Crone, Phil, Kocisky, Tomas, Zhao, Jeffrey, Perz, Bartek, Yu, Dian, Howard, Heidi, Bloniarz, Adam, Rae, Jack W., Lu, Han, Sifre, Laurent, Maggioni, Marcello, Alcober, Fred, Garrette, Dan, Barnes, Megan, Thakoor, Shantanu, Austin, Jacob, Barth-Maron, Gabriel, Wong, William, Joshi, Rishabh, Chaabouni, Rahma, Fatiha, Deeni, Ahuja, Arun, Tomar, Gaurav Singh, Senter, Evan, Chadwick, Martin, Kornakov, Ilya, Attaluri, Nithya, Iturrate, Iñaki, Liu, Ruibo, Li, Yunxuan, Cogan, Sarah, Chen, Jeremy, Jia, Chao, Gu, Chenjie, Zhang, Qiao, Grimstad, Jordan, Hartman, Ale Jakse, Garcia, Xavier, Pillai, Thanumalayan Sankaranarayana, Devlin, Jacob, Laskin, Michael, Casas, Diego de Las, Valter, Dasha, Tao, Connie, Blanco, Lorenzo, Badia, Adrià Puigdomènech, Reitter, David, Chen, Mianna, Brennan, Jenny, Rivera, Clara, Brin, Sergey, Iqbal, Shariq, Surita, Gabriela, Labanowski, Jane, Rao, Abhi, Winkler, Stephanie, Parisotto, Emilio, Gu, Yiming, Olszewska, Kate, Addanki, Ravi, Miech, Antoine, Louis, Annie, Teplyashin, Denis, Brown, Geoff, Catt, Elliot, Balaguer, Jan, Xiang, Jackie, Wang, Pidong, Ashwood, Zoe, Briukhov, Anton, Webson, Albert, Ganapathy, Sanjay, Sanghavi, Smit, Kannan, Ajay, Chang, Ming-Wei, Stjerngren, Axel, Djolonga, Josip, Sun, Yuting, Bapna, Ankur, Aitchison, Matthew, Pejman, Pedram, Michalewski, Henryk, Yu, Tianhe, Wang, Cindy, Love, Juliette, Ahn, Junwhan, Bloxwich, Dawn, Han, Kehang, Humphreys, Peter, Sellam, Thibault, Bradbury, James, Godbole, Varun, Samangooei, Sina, Damoc, Bogdan, Kaskasoli, Alex, Arnold, Sébastien M. 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Carpenter, John, Papamakarios, George, Kemp, Rupert, Kafle, Sushant, Grunina, Tanya, Sinha, Rishika, Talbert, Alice, Wu, Diane, Owusu-Afriyie, Denese, Thornton, Chloe, Pont-Tuset, Jordi, Narayana, Pradyumna, Li, Jing, Fatehi, Saaber, Wieting, John, Ajmeri, Omar, Uria, Benigno, Ko, Yeongil, Knight, Laura, Héliou, Amélie, Niu, Ning, Gu, Shane, Pang, Chenxi, Li, Yeqing, Levine, Nir, Stolovich, Ariel, Santamaria-Fernandez, Rebeca, Goenka, Sonam, Yustalim, Wenny, Strudel, Robin, Elqursh, Ali, Deck, Charlie, Lee, Hyo, Li, Zonglin, Levin, Kyle, Hoffmann, Raphael, Holtmann-Rice, Dan, Bachem, Olivier, Arora, Sho, Koh, Christy, Yeganeh, Soheil Hassas, Põder, Siim, Tariq, Mukarram, Sun, Yanhua, Ionita, Lucian, Seyedhosseini, Mojtaba, Tafti, Pouya, Liu, Zhiyu, Gulati, Anmol, Liu, Jasmine, Ye, Xinyu, Chrzaszcz, Bart, Wang, Lily, Sethi, Nikhil, Li, Tianrun, Brown, Ben, Singh, Shreya, Fan, Wei, Parisi, Aaron, Stanton, Joe, Koverkathu, Vinod, Choquette-Choo, Christopher A., Li, Yunjie, Lu, TJ, Shroff, Prakash, Varadarajan, Mani, Bahargam, Sanaz, Willoughby, Rob, Gaddy, David, Desjardins, Guillaume, Cornero, Marco, Robenek, Brona, Mittal, Bhavishya, Albrecht, Ben, Shenoy, Ashish, Moiseev, Fedor, Jacobsson, Henrik, Ghaffarkhah, Alireza, Rivière, Morgane, Walton, Alanna, Crepy, Clément, Parrish, Alicia, Zhou, Zongwei, Farabet, Clement, Radebaugh, Carey, Srinivasan, Praveen, van der Salm, Claudia, Fidjeland, Andreas, Scellato, Salvatore, Latorre-Chimoto, Eri, Klimczak-Plucińska, Hanna, Bridson, David, de Cesare, Dario, Hudson, Tom, Mendolicchio, Piermaria, Walker, Lexi, Morris, Alex, Mauger, Matthew, Guseynov, Alexey, Reid, Alison, Odoom, Seth, Loher, Lucia, Cotruta, Victor, Yenugula, Madhavi, Grewe, Dominik, Petrushkina, Anastasia, Duerig, Tom, Sanchez, Antonio, Yadlowsky, Steve, Shen, Amy, Globerson, Amir, Webb, Lynette, Dua, Sahil, Li, Dong, Bhupatiraju, Surya, Hurt, Dan, Qureshi, Haroon, Agarwal, Ananth, Shani, Tomer, Eyal, Matan, Khare, Anuj, Belle, Shreyas Rammohan, Wang, Lei, Tekur, Chetan, Kale, Mihir Sanjay, Wei, Jinliang, Sang, Ruoxin, Saeta, Brennan, Liechty, Tyler, Sun, Yi, Zhao, Yao, Lee, Stephan, Nayak, Pandu, Fritz, Doug, Vuyyuru, Manish Reddy, Aslanides, John, Vyas, Nidhi, Wicke, Martin, Ma, Xiao, Eltyshev, Evgenii, Martin, Nina, Cate, Hardie, Manyika, James, Amiri, Keyvan, Kim, Yelin, Xiong, Xi, Kang, Kai, Luisier, Florian, Tripuraneni, Nilesh, Madras, David, Guo, Mandy, Waters, Austin, Wang, Oliver, Ainslie, Joshua, Baldridge, Jason, Zhang, Han, Pruthi, Garima, Bauer, Jakob, Yang, Feng, Mansour, Riham, Gelman, Jason, Xu, Yang, Polovets, George, Liu, Ji, Cai, Honglong, Chen, Warren, Sheng, XiangHai, Xue, Emily, Ozair, Sherjil, Angermueller, Christof, Li, Xiaowei, Sinha, Anoop, Wang, Weiren, Wiesinger, Julia, Koukoumidis, Emmanouil, Tian, Yuan, Iyer, Anand, Gurumurthy, Madhu, Goldenson, Mark, Shah, Parashar, Blake, MK, Yu, Hongkun, Urbanowicz, Anthony, Palomaki, Jennimaria, Fernando, Chrisantha, Durden, Ken, Mehta, Harsh, Momchev, Nikola, Rahimtoroghi, Elahe, Georgaki, Maria, Raul, Amit, Ruder, Sebastian, Redshaw, Morgan, Lee, Jinhyuk, Zhou, Denny, Jalan, Komal, Li, Dinghua, Hechtman, Blake, Schuh, Parker, Nasr, Milad, Milan, Kieran, Mikulik, Vladimir, Franco, Juliana, Green, Tim, Nguyen, Nam, Kelley, Joe, Mahendru, Aroma, Hu, Andrea, Howland, Joshua, Vargas, Ben, Hui, Jeffrey, Bansal, Kshitij, Rao, Vikram, Ghiya, Rakesh, Wang, Emma, Ye, Ke, Sarr, Jean Michel, Preston, Melanie Moranski, Elish, Madeleine, Li, Steve, Kaku, Aakash, Gupta, Jigar, Pasupat, Ice, Juan, Da-Cheng, Someswar, Milan, M., Tejvi, Chen, Xinyun, Amini, Aida, Fabrikant, Alex, Chu, Eric, Dong, Xuanyi, Muthal, Amruta, Buthpitiya, Senaka, Jauhari, Sarthak, Khandelwal, Urvashi, Hitron, Ayal, Ren, Jie, Rinaldi, Larissa, Drath, Shahar, Dabush, Avigail, Jiang, Nan-Jiang, Godhia, Harshal, Sachs, Uli, Chen, Anthony, Fan, Yicheng, Taitelbaum, Hagai, Noga, Hila, Dai, Zhuyun, Wang, James, Hamer, Jenny, Ferng, Chun-Sung, Elkind, Chenel, Atias, Aviel, Lee, Paulina, Listík, Vít, Carlen, Mathias, van de Kerkhof, Jan, Pikus, Marcin, Zaher, Krunoslav, Müller, Paul, Zykova, Sasha, Stefanec, Richard, Gatsko, Vitaly, Hirnschall, Christoph, Sethi, Ashwin, Xu, Xingyu Federico, Ahuja, Chetan, Tsai, Beth, Stefanoiu, Anca, Feng, Bo, Dhandhania, Keshav, Katyal, Manish, Gupta, Akshay, Parulekar, Atharva, Pitta, Divya, Zhao, Jing, Bhatia, Vivaan, Bhavnani, Yashodha, Alhadlaq, Omar, Li, Xiaolin, Danenberg, Peter, Tu, Dennis, Pine, Alex, Filippova, Vera, Ghosh, Abhipso, Limonchik, Ben, Urala, Bhargava, Lanka, Chaitanya Krishna, Clive, Derik, Li, Edward, Wu, Hao, Hongtongsak, Kevin, Li, Ianna, Thakkar, Kalind, Omarov, Kuanysh, Majmundar, Kushal, Alverson, Michael, Kucharski, Michael, Patel, Mohak, Jain, Mudit, Zabelin, Maksim, Pelagatti, Paolo, Kohli, Rohan, Kumar, Saurabh, Kim, Joseph, Sankar, Swetha, Shah, Vineet, Ramachandruni, Lakshmi, Zeng, Xiangkai, Bariach, Ben, Weidinger, Laura, Vu, Tu, Andreev, Alek, He, Antoine, Hui, Kevin, Kashem, Sheleem, Subramanya, Amar, Hsiao, Sissie, Hassabis, Demis, Kavukcuoglu, Koray, Sadovsky, Adam, Le, Quoc, Strohman, Trevor, Wu, Yonghui, Petrov, Slav, Dean, Jeffrey, and Vinyals, Oriol
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Computer Science - Computation and Language ,Computer Science - Artificial Intelligence ,Computer Science - Computer Vision and Pattern Recognition - Abstract
This report introduces a new family of multimodal models, Gemini, that exhibit remarkable capabilities across image, audio, video, and text understanding. The Gemini family consists of Ultra, Pro, and Nano sizes, suitable for applications ranging from complex reasoning tasks to on-device memory-constrained use-cases. Evaluation on a broad range of benchmarks shows that our most-capable Gemini Ultra model advances the state of the art in 30 of 32 of these benchmarks - notably being the first model to achieve human-expert performance on the well-studied exam benchmark MMLU, and improving the state of the art in every one of the 20 multimodal benchmarks we examined. We believe that the new capabilities of the Gemini family in cross-modal reasoning and language understanding will enable a wide variety of use cases. We discuss our approach toward post-training and deploying Gemini models responsibly to users through services including Gemini, Gemini Advanced, Google AI Studio, and Cloud Vertex AI.
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- 2023
6. Frontier Language Models are not Robust to Adversarial Arithmetic, or 'What do I need to say so you agree 2+2=5?
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Freeman, C. Daniel, Culp, Laura, Parisi, Aaron, Bileschi, Maxwell L, Elsayed, Gamaleldin F, Rizkowsky, Alex, Simpson, Isabelle, Alemi, Alex, Nova, Azade, Adlam, Ben, Bohnet, Bernd, Mishra, Gaurav, Sedghi, Hanie, Mordatch, Igor, Gur, Izzeddin, Lee, Jaehoon, Co-Reyes, JD, Pennington, Jeffrey, Xu, Kelvin, Swersky, Kevin, Mahajan, Kshiteej, Xiao, Lechao, Liu, Rosanne, Kornblith, Simon, Constant, Noah, Liu, Peter J., Novak, Roman, Qian, Yundi, Fiedel, Noah, and Sohl-Dickstein, Jascha
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Computer Science - Computation and Language ,Computer Science - Artificial Intelligence ,Computer Science - Computers and Society ,Computer Science - Machine Learning - Abstract
We introduce and study the problem of adversarial arithmetic, which provides a simple yet challenging testbed for language model alignment. This problem is comprised of arithmetic questions posed in natural language, with an arbitrary adversarial string inserted before the question is complete. Even in the simple setting of 1-digit addition problems, it is easy to find adversarial prompts that make all tested models (including PaLM2, GPT4, Claude2) misbehave, and even to steer models to a particular wrong answer. We additionally provide a simple algorithm for finding successful attacks by querying those same models, which we name "prompt inversion rejection sampling" (PIRS). We finally show that models can be partially hardened against these attacks via reinforcement learning and via agentic constitutional loops. However, we were not able to make a language model fully robust against adversarial arithmetic attacks.
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- 2023
7. Ethno-medicinal Practices among Pattharkatta Kanjars in Lucknow District of U.P.
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Mishra, Gaurav and Singh, Udai Pratap
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- 2017
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8. New Records of Lichenicolous Fungi Inhabiting Cladonia from India
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Akhilesh Kumar Maurya, Mishra, Gaurav K., Joseph, Siljo, and Upreti, Dalip K.
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- 2024
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9. New Lichenicolous Phyllopsora (Ramalinaceae) Species on Phaeophyscia from India
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Pooja Maurya, Mishra, Gaurav K., and Upreti, Dalip K.
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- 2024
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10. PaLM 2 Technical Report
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Anil, Rohan, Dai, Andrew M., Firat, Orhan, Johnson, Melvin, Lepikhin, Dmitry, Passos, Alexandre, Shakeri, Siamak, Taropa, Emanuel, Bailey, Paige, Chen, Zhifeng, Chu, Eric, Clark, Jonathan H., Shafey, Laurent El, Huang, Yanping, Meier-Hellstern, Kathy, Mishra, Gaurav, Moreira, Erica, Omernick, Mark, Robinson, Kevin, Ruder, Sebastian, Tay, Yi, Xiao, Kefan, Xu, Yuanzhong, Zhang, Yujing, Abrego, Gustavo Hernandez, Ahn, Junwhan, Austin, Jacob, Barham, Paul, Botha, Jan, Bradbury, James, Brahma, Siddhartha, Brooks, Kevin, Catasta, Michele, Cheng, Yong, Cherry, Colin, Choquette-Choo, Christopher A., Chowdhery, Aakanksha, Crepy, Clément, Dave, Shachi, Dehghani, Mostafa, Dev, Sunipa, Devlin, Jacob, Díaz, Mark, Du, Nan, Dyer, Ethan, Feinberg, Vlad, Feng, Fangxiaoyu, Fienber, Vlad, Freitag, Markus, Garcia, Xavier, Gehrmann, Sebastian, Gonzalez, Lucas, Gur-Ari, Guy, Hand, Steven, Hashemi, Hadi, Hou, Le, Howland, Joshua, Hu, Andrea, Hui, Jeffrey, Hurwitz, Jeremy, Isard, Michael, Ittycheriah, Abe, Jagielski, Matthew, Jia, Wenhao, Kenealy, Kathleen, Krikun, Maxim, Kudugunta, Sneha, Lan, Chang, Lee, Katherine, Lee, Benjamin, Li, Eric, Li, Music, Li, Wei, Li, YaGuang, Li, Jian, Lim, Hyeontaek, Lin, Hanzhao, Liu, Zhongtao, Liu, Frederick, Maggioni, Marcello, Mahendru, Aroma, Maynez, Joshua, Misra, Vedant, Moussalem, Maysam, Nado, Zachary, Nham, John, Ni, Eric, Nystrom, Andrew, Parrish, Alicia, Pellat, Marie, Polacek, Martin, Polozov, Alex, Pope, Reiner, Qiao, Siyuan, Reif, Emily, Richter, Bryan, Riley, Parker, Ros, Alex Castro, Roy, Aurko, Saeta, Brennan, Samuel, Rajkumar, Shelby, Renee, Slone, Ambrose, Smilkov, Daniel, So, David R., Sohn, Daniel, Tokumine, Simon, Valter, Dasha, Vasudevan, Vijay, Vodrahalli, Kiran, Wang, Xuezhi, Wang, Pidong, Wang, Zirui, Wang, Tao, Wieting, John, Wu, Yuhuai, Xu, Kelvin, Xu, Yunhan, Xue, Linting, Yin, Pengcheng, Yu, Jiahui, Zhang, Qiao, Zheng, Steven, Zheng, Ce, Zhou, Weikang, Zhou, Denny, Petrov, Slav, and Wu, Yonghui
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Computer Science - Computation and Language ,Computer Science - Artificial Intelligence - Abstract
We introduce PaLM 2, a new state-of-the-art language model that has better multilingual and reasoning capabilities and is more compute-efficient than its predecessor PaLM. PaLM 2 is a Transformer-based model trained using a mixture of objectives. Through extensive evaluations on English and multilingual language, and reasoning tasks, we demonstrate that PaLM 2 has significantly improved quality on downstream tasks across different model sizes, while simultaneously exhibiting faster and more efficient inference compared to PaLM. This improved efficiency enables broader deployment while also allowing the model to respond faster, for a more natural pace of interaction. PaLM 2 demonstrates robust reasoning capabilities exemplified by large improvements over PaLM on BIG-Bench and other reasoning tasks. PaLM 2 exhibits stable performance on a suite of responsible AI evaluations, and enables inference-time control over toxicity without additional overhead or impact on other capabilities. Overall, PaLM 2 achieves state-of-the-art performance across a diverse set of tasks and capabilities. When discussing the PaLM 2 family, it is important to distinguish between pre-trained models (of various sizes), fine-tuned variants of these models, and the user-facing products that use these models. In particular, user-facing products typically include additional pre- and post-processing steps. Additionally, the underlying models may evolve over time. Therefore, one should not expect the performance of user-facing products to exactly match the results reported in this report.
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- 2023
11. Ein theoretisches Framework des landwirtschaftlichen Wissensmanagementprozesses im Kontext der indischen Landwirtschaft
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Vangala, Ram Naresh Kumar, Mishra, Gaurav, Chaudhary, Sanjay, editor, Biradar, Chandrashekhar M., editor, Divakaran, Srikrishnan, editor, and Raval, Mehul S., editor
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- 2024
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12. Utilization of Agro-waste as a Reinforcement Material in Polymer Matrix for Biodegradable Packaging Applications
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Gautam, Shashi Bala, Dixit, Shobhit, Yadav, Vijay Laxmi, Mishra, Gaurav, Sawood, Ghazi Mohd, Singh, Neeta, Srivastava, Neha, Series Editor, Mishra, P. K., Series Editor, Pal, Dan Bahadur, editor, Rai, Ashutosh Kumar, editor, and Siddiqui, Samra, editor
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- 2024
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13. Bioenergy from Agro-waste: A Sustainable Solution for Energy Needs
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Singh, Neeta, Gautam, Shashi Bala, Sawood, Ghazi Mohd, Yadav, Vijay Laxmi, Mishra, Gaurav, Dixit, Shobhit, Gupta, S. K., Srivastava, Neha, Series Editor, Mishra, P. K., Series Editor, Pal, Dan Bahadur, editor, Rai, Ashutosh Kumar, editor, and Siddiqui, Samra, editor
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- 2024
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14. Physiological and molecular insights into the allelopathic effects on agroecosystems under changing environmental conditions
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Kumar, Narendra, Singh, Hukum, Giri, Krishna, Kumar, Amit, Joshi, Amit, Yadav, Shambhavi, Singh, Ranjeet, Bisht, Sarita, Kumari, Rama, Jeena, Neha, Khairakpam, Rowndel, and Mishra, Gaurav
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- 2024
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15. A stacked ensemble learning-based framework for mineral mapping using AVIRIS-NG hyperspectral image
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Giri, Ram Nivas, Janghel, Rekh Ram, Govil, Himanshu, and Mishra, Gaurav
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- 2024
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16. The Todas of Nilgiri Hills; Their Dietary Pattern and Body Constitution
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Mishra, Gaurav and Singh, Udai Pratap
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- 2011
17. Scaling Instruction-Finetuned Language Models
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Chung, Hyung Won, Hou, Le, Longpre, Shayne, Zoph, Barret, Tay, Yi, Fedus, William, Li, Yunxuan, Wang, Xuezhi, Dehghani, Mostafa, Brahma, Siddhartha, Webson, Albert, Gu, Shixiang Shane, Dai, Zhuyun, Suzgun, Mirac, Chen, Xinyun, Chowdhery, Aakanksha, Castro-Ros, Alex, Pellat, Marie, Robinson, Kevin, Valter, Dasha, Narang, Sharan, Mishra, Gaurav, Yu, Adams, Zhao, Vincent, Huang, Yanping, Dai, Andrew, Yu, Hongkun, Petrov, Slav, Chi, Ed H., Dean, Jeff, Devlin, Jacob, Roberts, Adam, Zhou, Denny, Le, Quoc V., and Wei, Jason
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Computer Science - Machine Learning ,Computer Science - Computation and Language - Abstract
Finetuning language models on a collection of datasets phrased as instructions has been shown to improve model performance and generalization to unseen tasks. In this paper we explore instruction finetuning with a particular focus on (1) scaling the number of tasks, (2) scaling the model size, and (3) finetuning on chain-of-thought data. We find that instruction finetuning with the above aspects dramatically improves performance on a variety of model classes (PaLM, T5, U-PaLM), prompting setups (zero-shot, few-shot, CoT), and evaluation benchmarks (MMLU, BBH, TyDiQA, MGSM, open-ended generation). For instance, Flan-PaLM 540B instruction-finetuned on 1.8K tasks outperforms PALM 540B by a large margin (+9.4% on average). Flan-PaLM 540B achieves state-of-the-art performance on several benchmarks, such as 75.2% on five-shot MMLU. We also publicly release Flan-T5 checkpoints, which achieve strong few-shot performance even compared to much larger models, such as PaLM 62B. Overall, instruction finetuning is a general method for improving the performance and usability of pretrained language models., Comment: Public checkpoints: https://huggingface.co/docs/transformers/model_doc/flan-t5
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- 2022
18. Evaluating genotypes for combining ability through diallel analysis in okra [Abelmoschus esculelltus (L.) Moench]
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Yadav, J.R., Kumar, Sunil, Mishra, Gaurav, Singh, B., Yadav, J.K., and Singh, S.P.
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- 2007
19. PaLI: A Jointly-Scaled Multilingual Language-Image Model
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Chen, Xi, Wang, Xiao, Changpinyo, Soravit, Piergiovanni, AJ, Padlewski, Piotr, Salz, Daniel, Goodman, Sebastian, Grycner, Adam, Mustafa, Basil, Beyer, Lucas, Kolesnikov, Alexander, Puigcerver, Joan, Ding, Nan, Rong, Keran, Akbari, Hassan, Mishra, Gaurav, Xue, Linting, Thapliyal, Ashish, Bradbury, James, Kuo, Weicheng, Seyedhosseini, Mojtaba, Jia, Chao, Ayan, Burcu Karagol, Riquelme, Carlos, Steiner, Andreas, Angelova, Anelia, Zhai, Xiaohua, Houlsby, Neil, and Soricut, Radu
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Computer Science - Computer Vision and Pattern Recognition ,Computer Science - Computation and Language - Abstract
Effective scaling and a flexible task interface enable large language models to excel at many tasks. We present PaLI (Pathways Language and Image model), a model that extends this approach to the joint modeling of language and vision. PaLI generates text based on visual and textual inputs, and with this interface performs many vision, language, and multimodal tasks, in many languages. To train PaLI, we make use of large pre-trained encoder-decoder language models and Vision Transformers (ViTs). This allows us to capitalize on their existing capabilities and leverage the substantial cost of training them. We find that joint scaling of the vision and language components is important. Since existing Transformers for language are much larger than their vision counterparts, we train a large, 4-billion parameter ViT (ViT-e) to quantify the benefits from even larger-capacity vision models. To train PaLI, we create a large multilingual mix of pretraining tasks, based on a new image-text training set containing 10B images and texts in over 100 languages. PaLI achieves state-of-the-art in multiple vision and language tasks (such as captioning, visual question-answering, scene-text understanding), while retaining a simple, modular, and scalable design., Comment: ICLR 2023 (Notable-top-5%)
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- 2022
20. Detailed investigation on x-ray emission from laser driven high-Z foils in a wide intensity range : role of conversion layer and reemission zone
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Mishra, Gaurav and Ghosh, Karabi
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Physics - Plasma Physics - Abstract
Detailed radiation hydrodynamic simulations are carried out to investigate x-ray emission process in four high-Z planar targets namely, tungsten (W), gold (Au), lead (Pb) and uranium (U) irradiated by 1 ns, 351 nm flat top laser pulses. A thorough zoning analysis is performed for all laser driven high-Z foils over a wide intensity range of $10^{12}-10^{15} W/cm^{2}$ with appropriately chosen photon energy range and recombination parameter. The resulting variation of conversion efficiency over the full intensity range exhibits an optimum for all materials which is explained by considering the characteristic emission contributions from two different regions of laser irradiated plasma, namely, conversion layer and remission zone. A new generalized single scaling relation based upon smooth broken power law is proposed for conversion efficiency variation along with the separate determination ($\eta_{S}$, $\eta_{M}$) in soft and hard/M-band x-ray regions. It has been observed that $\eta_{S}$ for Pb and W always lies in between that for Au and U for intensities smaller than $\sim 3\times 10^{13} W/cm^{2}$. On further increase in intensity, $\eta_{S}$ is observed to be maximum for Au and U whereas it is minimum for W. Significant contribution to M-band conversion efficiencies is observed in all elements for intensities higher than $\sim 2\times 10^{13} W/cm^{2}$ with maximum and minimum values attained by W and U, respectively. The results are explained by considering the contributions from the emission coefficients of all materials in both conversion layer and reemission zone up to corresponding photon cut-off energies at different laser intensities., Comment: Submitted to Physics of Plasmas
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- 2022
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21. Achieving Land Degradation Neutrality Through Tea Plantation: Future Prospect for Combating Climate Change in the Northeast Himalayan Region of India
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Modak, Kingshuk, Mishra, Gaurav, Saha, Saurav, Shakuntala, Ingudam, and Francaviglia, Rosa
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- 2023
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22. Identification of Disputed Documents, Fingerprints and Ballistics
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Tripathi, Shruti and Mishra, Gaurav
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- 2017
23. Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models
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Srivastava, Aarohi, Rastogi, Abhinav, Rao, Abhishek, Shoeb, Abu Awal Md, Abid, Abubakar, Fisch, Adam, Brown, Adam R., Santoro, Adam, Gupta, Aditya, Garriga-Alonso, Adrià, Kluska, Agnieszka, Lewkowycz, Aitor, Agarwal, Akshat, Power, Alethea, Ray, Alex, Warstadt, Alex, Kocurek, Alexander W., Safaya, Ali, Tazarv, Ali, Xiang, Alice, Parrish, Alicia, Nie, Allen, Hussain, Aman, Askell, Amanda, Dsouza, Amanda, Slone, Ambrose, Rahane, Ameet, Iyer, Anantharaman S., Andreassen, Anders, Madotto, Andrea, Santilli, Andrea, Stuhlmüller, Andreas, Dai, Andrew, La, Andrew, Lampinen, Andrew, Zou, Andy, Jiang, Angela, Chen, Angelica, Vuong, Anh, Gupta, Animesh, Gottardi, Anna, Norelli, Antonio, Venkatesh, Anu, Gholamidavoodi, Arash, Tabassum, Arfa, Menezes, Arul, Kirubarajan, Arun, Mullokandov, Asher, Sabharwal, Ashish, Herrick, Austin, Efrat, Avia, Erdem, Aykut, Karakaş, Ayla, Roberts, B. Ryan, Loe, Bao Sheng, Zoph, Barret, Bojanowski, Bartłomiej, Özyurt, Batuhan, Hedayatnia, Behnam, Neyshabur, Behnam, Inden, Benjamin, Stein, Benno, Ekmekci, Berk, Lin, Bill Yuchen, Howald, Blake, Orinion, Bryan, Diao, Cameron, Dour, Cameron, Stinson, Catherine, Argueta, Cedrick, Ramírez, César Ferri, Singh, Chandan, Rathkopf, Charles, Meng, Chenlin, Baral, Chitta, Wu, Chiyu, Callison-Burch, Chris, Waites, Chris, Voigt, Christian, Manning, Christopher D., Potts, Christopher, Ramirez, Cindy, Rivera, Clara E., Siro, Clemencia, Raffel, Colin, Ashcraft, Courtney, Garbacea, Cristina, Sileo, Damien, Garrette, Dan, Hendrycks, Dan, Kilman, Dan, Roth, Dan, Freeman, Daniel, Khashabi, Daniel, Levy, Daniel, González, Daniel Moseguí, Perszyk, Danielle, Hernandez, Danny, Chen, Danqi, Ippolito, Daphne, Gilboa, Dar, Dohan, David, Drakard, David, Jurgens, David, Datta, Debajyoti, Ganguli, Deep, Emelin, Denis, Kleyko, Denis, Yuret, Deniz, Chen, Derek, Tam, Derek, Hupkes, Dieuwke, Misra, Diganta, Buzan, Dilyar, Mollo, Dimitri Coelho, Yang, Diyi, Lee, Dong-Ho, Schrader, Dylan, Shutova, Ekaterina, Cubuk, Ekin Dogus, Segal, Elad, Hagerman, Eleanor, Barnes, Elizabeth, Donoway, Elizabeth, Pavlick, Ellie, Rodola, Emanuele, Lam, Emma, Chu, Eric, Tang, Eric, Erdem, Erkut, Chang, Ernie, Chi, Ethan A., Dyer, Ethan, Jerzak, Ethan, Kim, Ethan, Manyasi, Eunice Engefu, Zheltonozhskii, Evgenii, Xia, Fanyue, Siar, Fatemeh, Martínez-Plumed, Fernando, Happé, Francesca, Chollet, Francois, Rong, Frieda, Mishra, Gaurav, Winata, Genta Indra, de Melo, Gerard, Kruszewski, Germán, Parascandolo, Giambattista, Mariani, Giorgio, Wang, Gloria, Jaimovitch-López, Gonzalo, Betz, Gregor, Gur-Ari, Guy, Galijasevic, Hana, Kim, Hannah, Rashkin, Hannah, Hajishirzi, Hannaneh, Mehta, Harsh, Bogar, Hayden, Shevlin, Henry, Schütze, Hinrich, Yakura, Hiromu, Zhang, Hongming, Wong, Hugh Mee, Ng, Ian, Noble, Isaac, Jumelet, Jaap, Geissinger, Jack, Kernion, Jackson, Hilton, Jacob, Lee, Jaehoon, Fisac, Jaime Fernández, Simon, James B., Koppel, James, Zheng, James, Zou, James, Kocoń, Jan, Thompson, Jana, Wingfield, Janelle, Kaplan, Jared, Radom, Jarema, Sohl-Dickstein, Jascha, Phang, Jason, Wei, Jason, Yosinski, Jason, Novikova, Jekaterina, Bosscher, Jelle, Marsh, Jennifer, Kim, Jeremy, Taal, Jeroen, Engel, Jesse, Alabi, Jesujoba, Xu, Jiacheng, Song, Jiaming, Tang, Jillian, Waweru, Joan, Burden, John, Miller, John, Balis, John U., Batchelder, Jonathan, Berant, Jonathan, Frohberg, Jörg, Rozen, Jos, Hernandez-Orallo, Jose, Boudeman, Joseph, Guerr, Joseph, Jones, Joseph, Tenenbaum, Joshua B., Rule, Joshua S., Chua, Joyce, Kanclerz, Kamil, Livescu, Karen, Krauth, Karl, Gopalakrishnan, Karthik, Ignatyeva, Katerina, Markert, Katja, Dhole, Kaustubh D., Gimpel, Kevin, Omondi, Kevin, Mathewson, Kory, Chiafullo, Kristen, Shkaruta, Ksenia, Shridhar, Kumar, McDonell, Kyle, Richardson, Kyle, Reynolds, Laria, Gao, Leo, Zhang, Li, Dugan, Liam, Qin, Lianhui, Contreras-Ochando, Lidia, Morency, Louis-Philippe, Moschella, Luca, Lam, Lucas, Noble, Lucy, Schmidt, Ludwig, He, Luheng, Colón, Luis Oliveros, Metz, Luke, Şenel, Lütfi Kerem, Bosma, Maarten, Sap, Maarten, ter Hoeve, Maartje, Farooqi, Maheen, Faruqui, Manaal, Mazeika, Mantas, Baturan, Marco, Marelli, Marco, Maru, Marco, Quintana, Maria Jose Ramírez, Tolkiehn, Marie, Giulianelli, Mario, Lewis, Martha, Potthast, Martin, Leavitt, Matthew L., Hagen, Matthias, Schubert, Mátyás, Baitemirova, Medina Orduna, Arnaud, Melody, McElrath, Melvin, Yee, Michael A., Cohen, Michael, Gu, Michael, Ivanitskiy, Michael, Starritt, Michael, Strube, Michael, Swędrowski, Michał, Bevilacqua, Michele, Yasunaga, Michihiro, Kale, Mihir, Cain, Mike, Xu, Mimee, Suzgun, Mirac, Walker, Mitch, Tiwari, Mo, Bansal, Mohit, Aminnaseri, Moin, Geva, Mor, Gheini, Mozhdeh, T, Mukund Varma, Peng, Nanyun, Chi, Nathan A., Lee, Nayeon, Krakover, Neta Gur-Ari, Cameron, Nicholas, Roberts, Nicholas, Doiron, Nick, Martinez, Nicole, Nangia, Nikita, Deckers, Niklas, Muennighoff, Niklas, Keskar, Nitish Shirish, Iyer, Niveditha S., Constant, Noah, Fiedel, Noah, Wen, Nuan, Zhang, Oliver, Agha, Omar, Elbaghdadi, Omar, Levy, Omer, Evans, Owain, Casares, Pablo Antonio Moreno, Doshi, Parth, Fung, Pascale, Liang, Paul Pu, Vicol, Paul, Alipoormolabashi, Pegah, Liao, Peiyuan, Liang, Percy, Chang, Peter, Eckersley, Peter, Htut, Phu Mon, Hwang, Pinyu, Miłkowski, Piotr, Patil, Piyush, Pezeshkpour, Pouya, Oli, Priti, Mei, Qiaozhu, Lyu, Qing, Chen, Qinlang, Banjade, Rabin, Rudolph, Rachel Etta, Gabriel, Raefer, Habacker, Rahel, Risco, Ramon, Millière, Raphaël, Garg, Rhythm, Barnes, Richard, Saurous, Rif A., Arakawa, Riku, Raymaekers, Robbe, Frank, Robert, Sikand, Rohan, Novak, Roman, Sitelew, Roman, LeBras, Ronan, Liu, Rosanne, Jacobs, Rowan, Zhang, Rui, Salakhutdinov, Ruslan, Chi, Ryan, Lee, Ryan, Stovall, Ryan, Teehan, Ryan, Yang, Rylan, Singh, Sahib, Mohammad, Saif M., Anand, Sajant, Dillavou, Sam, Shleifer, Sam, Wiseman, Sam, Gruetter, Samuel, Bowman, Samuel R., Schoenholz, Samuel S., Han, Sanghyun, Kwatra, Sanjeev, Rous, Sarah A., Ghazarian, Sarik, Ghosh, Sayan, Casey, Sean, Bischoff, Sebastian, Gehrmann, Sebastian, Schuster, Sebastian, Sadeghi, Sepideh, Hamdan, Shadi, Zhou, Sharon, Srivastava, Shashank, Shi, Sherry, Singh, Shikhar, Asaadi, Shima, Gu, Shixiang Shane, Pachchigar, Shubh, Toshniwal, Shubham, Upadhyay, Shyam, Shyamolima, Debnath, Shakeri, Siamak, Thormeyer, Simon, Melzi, Simone, Reddy, Siva, Makini, Sneha Priscilla, Lee, Soo-Hwan, Torene, Spencer, Hatwar, Sriharsha, Dehaene, Stanislas, Divic, Stefan, Ermon, Stefano, Biderman, Stella, Lin, Stephanie, Prasad, Stephen, Piantadosi, Steven T., Shieber, Stuart M., Misherghi, Summer, Kiritchenko, Svetlana, Mishra, Swaroop, Linzen, Tal, Schuster, Tal, Li, Tao, Yu, Tao, Ali, Tariq, Hashimoto, Tatsu, Wu, Te-Lin, Desbordes, Théo, Rothschild, Theodore, Phan, Thomas, Wang, Tianle, Nkinyili, Tiberius, Schick, Timo, Kornev, Timofei, Tunduny, Titus, Gerstenberg, Tobias, Chang, Trenton, Neeraj, Trishala, Khot, Tushar, Shultz, Tyler, Shaham, Uri, Misra, Vedant, Demberg, Vera, Nyamai, Victoria, Raunak, Vikas, Ramasesh, Vinay, Prabhu, Vinay Uday, Padmakumar, Vishakh, Srikumar, Vivek, Fedus, William, Saunders, William, Zhang, William, Vossen, Wout, Ren, Xiang, Tong, Xiaoyu, Zhao, Xinran, Wu, Xinyi, Shen, Xudong, Yaghoobzadeh, Yadollah, Lakretz, Yair, Song, Yangqiu, Bahri, Yasaman, Choi, Yejin, Yang, Yichi, Hao, Yiding, Chen, Yifu, Belinkov, Yonatan, Hou, Yu, Hou, Yufang, Bai, Yuntao, Seid, Zachary, Zhao, Zhuoye, Wang, Zijian, Wang, Zijie J., Wang, Zirui, and Wu, Ziyi
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Computer Science - Computation and Language ,Computer Science - Artificial Intelligence ,Computer Science - Computers and Society ,Computer Science - Machine Learning ,Statistics - Machine Learning - Abstract
Language models demonstrate both quantitative improvement and new qualitative capabilities with increasing scale. Despite their potentially transformative impact, these new capabilities are as yet poorly characterized. In order to inform future research, prepare for disruptive new model capabilities, and ameliorate socially harmful effects, it is vital that we understand the present and near-future capabilities and limitations of language models. To address this challenge, we introduce the Beyond the Imitation Game benchmark (BIG-bench). BIG-bench currently consists of 204 tasks, contributed by 450 authors across 132 institutions. Task topics are diverse, drawing problems from linguistics, childhood development, math, common-sense reasoning, biology, physics, social bias, software development, and beyond. BIG-bench focuses on tasks that are believed to be beyond the capabilities of current language models. We evaluate the behavior of OpenAI's GPT models, Google-internal dense transformer architectures, and Switch-style sparse transformers on BIG-bench, across model sizes spanning millions to hundreds of billions of parameters. In addition, a team of human expert raters performed all tasks in order to provide a strong baseline. Findings include: model performance and calibration both improve with scale, but are poor in absolute terms (and when compared with rater performance); performance is remarkably similar across model classes, though with benefits from sparsity; tasks that improve gradually and predictably commonly involve a large knowledge or memorization component, whereas tasks that exhibit "breakthrough" behavior at a critical scale often involve multiple steps or components, or brittle metrics; social bias typically increases with scale in settings with ambiguous context, but this can be improved with prompting., Comment: 27 pages, 17 figures + references and appendices, repo: https://github.com/google/BIG-bench
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- 2022
24. PaLM: Scaling Language Modeling with Pathways
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Chowdhery, Aakanksha, Narang, Sharan, Devlin, Jacob, Bosma, Maarten, Mishra, Gaurav, Roberts, Adam, Barham, Paul, Chung, Hyung Won, Sutton, Charles, Gehrmann, Sebastian, Schuh, Parker, Shi, Kensen, Tsvyashchenko, Sasha, Maynez, Joshua, Rao, Abhishek, Barnes, Parker, Tay, Yi, Shazeer, Noam, Prabhakaran, Vinodkumar, Reif, Emily, Du, Nan, Hutchinson, Ben, Pope, Reiner, Bradbury, James, Austin, Jacob, Isard, Michael, Gur-Ari, Guy, Yin, Pengcheng, Duke, Toju, Levskaya, Anselm, Ghemawat, Sanjay, Dev, Sunipa, Michalewski, Henryk, Garcia, Xavier, Misra, Vedant, Robinson, Kevin, Fedus, Liam, Zhou, Denny, Ippolito, Daphne, Luan, David, Lim, Hyeontaek, Zoph, Barret, Spiridonov, Alexander, Sepassi, Ryan, Dohan, David, Agrawal, Shivani, Omernick, Mark, Dai, Andrew M., Pillai, Thanumalayan Sankaranarayana, Pellat, Marie, Lewkowycz, Aitor, Moreira, Erica, Child, Rewon, Polozov, Oleksandr, Lee, Katherine, Zhou, Zongwei, Wang, Xuezhi, Saeta, Brennan, Diaz, Mark, Firat, Orhan, Catasta, Michele, Wei, Jason, Meier-Hellstern, Kathy, Eck, Douglas, Dean, Jeff, Petrov, Slav, and Fiedel, Noah
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Computer Science - Computation and Language - Abstract
Large language models have been shown to achieve remarkable performance across a variety of natural language tasks using few-shot learning, which drastically reduces the number of task-specific training examples needed to adapt the model to a particular application. To further our understanding of the impact of scale on few-shot learning, we trained a 540-billion parameter, densely activated, Transformer language model, which we call Pathways Language Model PaLM. We trained PaLM on 6144 TPU v4 chips using Pathways, a new ML system which enables highly efficient training across multiple TPU Pods. We demonstrate continued benefits of scaling by achieving state-of-the-art few-shot learning results on hundreds of language understanding and generation benchmarks. On a number of these tasks, PaLM 540B achieves breakthrough performance, outperforming the finetuned state-of-the-art on a suite of multi-step reasoning tasks, and outperforming average human performance on the recently released BIG-bench benchmark. A significant number of BIG-bench tasks showed discontinuous improvements from model scale, meaning that performance steeply increased as we scaled to our largest model. PaLM also has strong capabilities in multilingual tasks and source code generation, which we demonstrate on a wide array of benchmarks. We additionally provide a comprehensive analysis on bias and toxicity, and study the extent of training data memorization with respect to model scale. Finally, we discuss the ethical considerations related to large language models and discuss potential mitigation strategies.
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- 2022
25. Scaling Up Models and Data with $\texttt{t5x}$ and $\texttt{seqio}$
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Roberts, Adam, Chung, Hyung Won, Levskaya, Anselm, Mishra, Gaurav, Bradbury, James, Andor, Daniel, Narang, Sharan, Lester, Brian, Gaffney, Colin, Mohiuddin, Afroz, Hawthorne, Curtis, Lewkowycz, Aitor, Salcianu, Alex, van Zee, Marc, Austin, Jacob, Goodman, Sebastian, Soares, Livio Baldini, Hu, Haitang, Tsvyashchenko, Sasha, Chowdhery, Aakanksha, Bastings, Jasmijn, Bulian, Jannis, Garcia, Xavier, Ni, Jianmo, Chen, Andrew, Kenealy, Kathleen, Clark, Jonathan H., Lee, Stephan, Garrette, Dan, Lee-Thorp, James, Raffel, Colin, Shazeer, Noam, Ritter, Marvin, Bosma, Maarten, Passos, Alexandre, Maitin-Shepard, Jeremy, Fiedel, Noah, Omernick, Mark, Saeta, Brennan, Sepassi, Ryan, Spiridonov, Alexander, Newlan, Joshua, and Gesmundo, Andrea
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Computer Science - Machine Learning ,Computer Science - Computation and Language - Abstract
Recent neural network-based language models have benefited greatly from scaling up the size of training datasets and the number of parameters in the models themselves. Scaling can be complicated due to various factors including the need to distribute computation on supercomputer clusters (e.g., TPUs), prevent bottlenecks when infeeding data, and ensure reproducible results. In this work, we present two software libraries that ease these issues: $\texttt{t5x}$ simplifies the process of building and training large language models at scale while maintaining ease of use, and $\texttt{seqio}$ provides a task-based API for simple creation of fast and reproducible training data and evaluation pipelines. These open-source libraries have been used to train models with hundreds of billions of parameters on datasets with multiple terabytes of training data. Along with the libraries, we release configurations and instructions for T5-like encoder-decoder models as well as GPT-like decoder-only architectures. $\texttt{t5x}$ and $\texttt{seqio}$ are open source and available at https://github.com/google-research/t5x and https://github.com/google/seqio, respectively.
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- 2022
26. Impact of Land Uses on Soil Organic Carbon Dynamics in the Indian Himalayan Region
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Das, Anshuman, Mishra, Gaurav, Lakra, Pramod Chand, Kumar, Sanjeev, Mishra, Shambhu Nath, Mishra, Gaurav, editor, Giri, Krishna, editor, Nath, Arun Jyoti, editor, and Francaviglia, Rosa, editor
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- 2023
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27. AFOLU Sectors of North East India and Their Potential for Soil Carbon Storage
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Modak, Kingshuk, Guru, Nibedita, Mishra, Gaurav, Jangir, Abhishek, Mishra, Gaurav, editor, Giri, Krishna, editor, Nath, Arun Jyoti, editor, and Francaviglia, Rosa, editor
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- 2023
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28. Soil Organic Carbon Modeling in Indian Eastern Himalayan Region: A Review of Case Studies
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Mishra, Gaurav, Francaviglia, Rosa, Mishra, Gaurav, editor, Giri, Krishna, editor, Nath, Arun Jyoti, editor, and Francaviglia, Rosa, editor
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- 2023
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29. Effect of soft and hard x-rays on shock propagation, preheating and ablation characteristics in pure and doped Be ablators
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Ghosh, Karabi and Mishra, Gaurav
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Physics - Plasma Physics - Abstract
In this paper, we analyze the performance of pure and doped Be ablators used for Inertial Confinement Fusion (ICF) pellets in terms of shock velocity, shock breakout temperature, preheat temperature and mass ablation rate through radiation hydrodynamic (RHD) simulations. For this study, we apply a constant radiation profile (drive temperatures varying from 120 - 200 eV) consisting of a low frequency Planck spectrum (soft x-rays) and a high frequency Gaussian spectrum (hard x-rays, commonly termed as "M-band") on a planar foil of the ablator. The fraction of energy density in the hard x-ray spectrum ($\alpha$) has been varied from 0 to 0.25. The predominant effect of hard x-rays is to preheat the ablator ahead of the shock front. Steady rise in preheat temperature and shock breakout temperature is observed on increasing the fraction of hard x-rays. Preheating can be mitigated by doping Be with a mid-Z element Cu as its opacity is much higher in the high frequency region. On doping Be with 1\% Cu, the shock velocities decrease slightly compared to pure Be. However, higher shock velocities are observed on increasing the fraction of M-band. We observe significant decrease in shock breakout and maximum preheat temperature in doped Be foil. Steady rise in these temperatures is observed on increasing $\alpha$. We have proposed new scaling relations for shock velocity, shock breakout temperature, maximum preheat temperature and mass ablation rate with the radiation temperature and the fraction of M-band energy density in both pure and doped Be ablators. In terms of ablator performance, Cu doped Be ablator is found to be superior to pure Be. Though doping significantly reduces preheating, the mass ablation rates are nearly unaffected., Comment: 32 pages, 34 figures, submitted to the journal "High Energy Density Physics"
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- 2021
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30. Assessment of prevalence and distribution of congenital missing teeth among patients visiting tertiary care hospital: A radiographic study
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Arif, Khushboo, Gupta, Vinay Kumar, Mishra, Gaurav, Kumar, Sumit, Pai Khot, Atrey, Bhatia, Sonal, Patil, Ranjit kumar, Singh, Abhishek, and Imran Khan, Mohammad
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- 2024
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31. Biosensors for Neurodegenerative Diseases
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Mishra, Gaurav, primary, Maurya, Anand, additional, Kumar Singh, Anurag, additional, Talebi, Marjan, additional, Awasthi, Rajendra, additional, and Kumar Nandi, Manmath, additional
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- 2023
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32. Molecular Genetics, Volumes 1 and 2
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Mishra, Gaurav
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- 2012
33. A Theoretical Framework of Agricultural Knowledge Management Process in the Indian Agriculture Context
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Vangala, Ram Naresh Kumar, Mishra, Gaurav, Kacprzyk, Janusz, Series Editor, Chaudhary, Sanjay, editor, Biradar, Chandrashekhar M., editor, Divakaran, Srikrishnan, editor, and Raval, Mehul S., editor
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- 2023
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34. A taxonomic revision of lichen genus Phaeophyscia Moberg (Physciaceae) with new records from India
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Maurya, Pooja, Mishra, Gaurav K., and Upreti, Dalip K.
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- 2024
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35. Identification of key altered/weathered minerals near to the base metal mineral in Jahazpur, India using AVIRIS-NG data
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Mishra, Gaurav, Govil, Himanshu, Guha, Arindam, and Rajendran, Sankaran
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- 2024
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36. Comparative evaluation of airborne AVIRIS-NG and spaceborne PRISMA hyperspectral data in identification and mapping of altered/weathered minerals in Jahazpur, Rajasthan
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Mishra, Gaurav, Govil, Himanshu, Guha, Arindam, Kumar, Hrishikesh, Kumar, Shashi, and Mukherjee, Sudipta
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- 2024
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37. Evaluation of machine learning techniques with AVIRIS-NG dataset in the identification and mapping of minerals
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Agrawal, Neelam, Govil, Himanshu, Chatterjee, Snehamoy, Mishra, Gaurav, and Mukherjee, Sudipta
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- 2024
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38. Path coefficient analysis in garlic (Allium sativum L.)
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Yadav, J.R., Singh, S.P., Ramadhar, Mishra, Gaurav, and Yadav, J.K.
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- 2007
39. A generalized multi-upgradation SRGM considering uncertainty of random field operating environments
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Mishra, Gaurav, Kapur, P. K., and Aggarwal, Anu G.
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- 2023
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40. Framework for an Area-Based Development Approach for Predicted Urban Sprawl in Delhi City
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Kumar Mishra, Gaurav, primary and Deshmukh, Amit M., additional
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- 2023
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41. Explainable Disease Classification via weakly-supervised segmentation
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Joshi, Aniket, Mishra, Gaurav, and Sivaswamy, Jayanthi
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Computer Science - Computer Vision and Pattern Recognition - Abstract
Deep learning based approaches to Computer Aided Diagnosis (CAD) typically pose the problem as an image classification (Normal or Abnormal) problem. These systems achieve high to very high accuracy in specific disease detection for which they are trained but lack in terms of an explanation for the provided decision/classification result. The activation maps which correspond to decisions do not correlate well with regions of interest for specific diseases. This paper examines this problem and proposes an approach which mimics the clinical practice of looking for an evidence prior to diagnosis. A CAD model is learnt using a mixed set of information: class labels for the entire training set of images plus a rough localisation of suspect regions as an extra input for a smaller subset of training images for guiding the learning. The proposed approach is illustrated with detection of diabetic macular edema (DME) from OCT slices. Results of testing on on a large public dataset show that with just a third of images with roughly segmented fluid filled regions, the classification accuracy is on par with state of the art methods while providing a good explanation in the form of anatomically accurate heatmap /region of interest. The proposed solution is then adapted to Breast Cancer detection from mammographic images. Good evaluation results on public datasets underscores the generalisability of the proposed solution.
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- 2020
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42. Viable land use options to achieve multiple ecosystem services in the Eastern Himalayas of India
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Rawat, Deepa, primary, Mishra, Gaurav, additional, and Francaviglia, Rosa, additional
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- 2023
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43. Climate-resilient pathways and nature-based solutions to reduce vulnerabilities to climate change in the Indian Himalayan Region
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Meetei, Kambam Boxen, primary, Tsopoe, Meribeni, additional, Giri, Krishna, additional, Mishra, Gaurav, additional, Verma, Praveen Kumar, additional, and Rohatgi, Deepika, additional
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- 2023
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44. Soil Nitrogen Dynamics and Management in Agroforestry Systems for Ecological Sustainability
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Rawat, Deepa, Mukhopadhyay, Debaaditya, Mishra, Gaurav, Bijalwan, Arvind, Panwar, Pankaj, editor, Shukla, Gopal, editor, Bhat, Jahangeer A., editor, and Chakravarty, Sumit, editor
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- 2022
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45. Prospects and Challenges of Bio-Nanomaterials for Wastewater Treatment
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Sati, Meenakshi, Sharma, Vishwanath, Goswami, Anup Jyoti, Giri, Krishna, Mishra, Gaurav, Singh, V. P., Editor-in-Chief, Berndtsson, R., Editorial Board Member, Rodrigues, L. N., Editorial Board Member, Sarma, Arup Kumar, Editorial Board Member, Sherif, M. M., Editorial Board Member, Sivakumar, B., Editorial Board Member, Zhang, Q., Editorial Board Member, Rai, Jai Prakash Narain, editor, and Saraswat, Shweta, editor
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- 2022
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46. Performance evaluation of native plant growth-promoting rhizobacteria for paddy yield enhancement in the jhum fields of Mokokchung, Nagaland, North East India
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Giri, Krishna, Mishra, Gaurav, Chandra Suyal, Deep, Kumar, Narendra, Doley, Bhanushree, Das, Niren, Baruah, Rupjyoti C., Bhattacharyya, Rajarshi, and Bora, Navajyoti
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- 2023
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47. Short-Term Effects of Bamboo Plantation on Soil Carbon Fractions, Carbon and Nitrogen Stocks in Eastern Himalayas, India
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Semy, Khikeya, Mishra, Gaurav, and Francaviglia, Rosa
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- 2022
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48. Comprehensive Analysis of Uncertainty Quantification for the 58Ni(n,p)58Co Reaction Cross section.
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Choudhary, Mahesh, primary, Sharma, Aman, additional, Singh, Namrata, additional, Upadhyay, Mahima, additional, Dubey, Punit, additional, Gandhi, Aman, additional, Hingu, Akash Manishbhai, additional, Mishra, Gaurav, additional, De, Sukanya, additional, Danu, L.S., additional, Kumar, Ajay, additional, Thomas, R.G., additional, Sood, Saurav, additional, Prasad, Sajin, additional, Mukherjee, Surjit, additional, Ruskov, Ivan Nikolov, additional, Kopatch, Yuri, additional, and Kumar, A., additional
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- 2024
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49. Radiologist’s approach in diagnosing fronto-ethmoidal meningoencephalocele in an adult: a case report
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Sood, Anshul, primary, Mishra, Gaurav Vedprakash, additional, Kashikar, Shivali, additional, Gupta, Roohi, additional, Shelar, Sheetal, additional, and Khandelwal, Shreya, additional
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- 2024
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50. Role of End Plate Changes and Paraspinal Muscle Pathology in Lower Back Pain: A Narrative Review
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Suryadevara, Manasa, primary, Mishra, Gaurav V, additional, Parihar, Pratapsingh, additional, Javvaji, Chaitanya Kumar, additional, Sood, Anshul, additional, Reddy, Harshitha, additional, Reddy, Naramreddy sudheesh, additional, and Shelar, Sheetal S, additional
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- 2024
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