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7 results on '"Liu, Zachary"'

1. OmniBench: Towards The Future of Universal Omni-Language Models

Author

Li, Yizhi, Zhang, Ge, Ma, Yinghao, Yuan, Ruibin, Zhu, Kang, Guo, Hangyu, Liang, Yiming, Liu, Jiaheng, Wang, Zekun, Yang, Jian, Wu, Siwei, Qu, Xingwei, Shi, Jinjie, Zhang, Xinyue, Yang, Zhenzhu, Wang, Xiangzhou, Zhang, Zhaoxiang, Liu, Zachary, Benetos, Emmanouil, Huang, Wenhao, and Lin, Chenghua

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition
Abstract

Recent advancements in multimodal large language models (MLLMs) have aimed to integrate and interpret data across diverse modalities. However, the capacity of these models to concurrently process and reason about multiple modalities remains inadequately explored, partly due to the lack of comprehensive modality-wise benchmarks. We introduce OmniBench, a novel benchmark designed to rigorously evaluate models' ability to recognize, interpret, and reason across visual, acoustic, and textual inputs simultaneously. We define models capable of such tri-modal processing as omni-language models (OLMs). OmniBench is distinguished by high-quality human annotations, ensuring that accurate responses require integrated understanding and reasoning across all three modalities. Our main findings reveal that: i) most OLMs exhibit critical limitations in instruction-following and reasoning capabilities within tri-modal contexts; and ii) most baselines models perform poorly (below 50\% accuracy) even when provided with alternative textual representations of images or/and audio. These results suggest that the ability to construct a consistent context from text, image, and audio is often overlooked in existing MLLM training paradigms. To address this gap, we curate an instruction tuning dataset of 84.5K training samples, OmniInstruct, for training OLMs to adapt to multimodal contexts. We advocate for future research to focus on developing more robust tri-modal integration techniques and training strategies to enhance OLM performance across diverse modalities. The codes and live leaderboard could be found at https://m-a-p.ai/OmniBench.

Published
2024

2. LIME: Less Is More for MLLM Evaluation

Author

Zhu, King, Zang, Qianbo, Jia, Shian, Wu, Siwei, Fang, Feiteng, Li, Yizhi, Gavin, Shawn, Zheng, Tuney, Guo, Jiawei, Li, Bo, Wu, Haoning, Qu, Xingwei, Yang, Jian, Liu, Zachary, Yue, Xiang, Liu, J. H., Lin, Chenghua, Yang, Min, Ni, Shiwen, Huang, Wenhao, and Zhang, Ge

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence
Abstract

Multimodal Large Language Models (MLLMs) are evaluated on various benchmarks, such as image captioning, visual question answering, and reasoning. However, many of these benchmarks include overly simple or uninformative samples, complicating the effective distinction of different MLLMs' performance. Furthermore, evaluating models across numerous benchmarks incurs a significant computational burden. To address these issues, we propose LIME (Less Is More for MLLM Evaluation), a refined and efficient benchmark curated through a semi-automated pipeline. This pipeline filters out uninformative samples and eliminates answer leakage by focusing on tasks that necessitate image-based understanding. Our experiments indicate that LIME reduces the number of samples by 76% and evaluation time by 77%, while also providing a more effective means of distinguishing the capabilities of different models. Notably, we find that traditional automatic metrics, such as CIDEr, are inadequate for assessing MLLMs' captioning performance; excluding the caption task score yields a more accurate reflection of overall model performance. All code and data are available at https://github.com/kangreen0210/LIME.

Published
2024

3. From Ad Identifiers to Global Privacy Control: The Status Quo and Future of Opting Out of Ad Tracking on Android

Author

Zimmeck, Sebastian, Aggarwal, Nishant, Liu, Zachary, and Kollnig, Konrad

Subjects
Computer Science - Cryptography and Security, Computer Science - Computers and Society
Abstract

Apps and their integrated third-party libraries often collect personal information from Android users for personalizing ads. This practice can be privacy-invasive. Users can limit ad tracking on Android via the AdID setting; further, the California Consumer Privacy Act (CCPA) gives user an opt-out right via Global Privacy Control (GPC). However, neither of these two privacy controls have been studied before as to whether they help Android users exercise their legally mandated opt-out right. In response, we evaluate how many Android apps are subject to the CCPA opt-out right and find it applicable to approximately 70% of apps on the top free app lists of the US Google Play Store. Our dynamic analysis of 1,811 apps from these lists shows that neither the AdID setting nor GPC effectively prevents the selling and sharing of personal information in California. For example, when disabling the AdID and sending GPC signals to the most common ad tracking domain in our dataset that implements the US Privacy String, only 4% of apps connecting to the domain indicate the opt-out status. To mitigate this shortcoming, Android's AdID setting should be evolved towards a universal GPC setting as part of Google's Privacy Sandbox.

Published
2024

7. Expansion of the global RNA virome reveals diverse clades of bacteriophages

Author

Uri Neri, Yuri I. Wolf, Simon Roux, Antonio Pedro Camargo, Benjamin Lee, Darius Kazlauskas, I. Min Chen, Natalia Ivanova, Lisa Zeigler Allen, David Paez-Espino, Donald A. Bryant, Devaki Bhaya, Mart Krupovic, Valerian V. Dolja, Nikos C. Kyrpides, Eugene V. Koonin, Uri Gophna, Adrienne B. Narrowe, Alexander J. Probst, Alexander Sczyrba, Annegret Kohler, Armand Séguin, Ashley Shade, Barbara J. Campbell, Björn D. Lindahl, Brandi Kiel Reese, Breanna M. Roque, Chris DeRito, Colin Averill, Daniel Cullen, David A.C. Beck, David A. Walsh, David M. Ward, Dongying Wu, Emiley Eloe-Fadrosh, Eoin L. Brodie, Erica B. Young, Erik A. Lilleskov, Federico J. Castillo, Francis M. Martin, Gary R. LeCleir, Graeme T. Attwood, Hinsby Cadillo-Quiroz, Holly M. Simon, Ian Hewson, Igor V. Grigoriev, James M. Tiedje, Janet K. Jansson, Janey Lee, Jean S. VanderGheynst, Jeff Dangl, Jeff S. Bowman, Jeffrey L. Blanchard, Jennifer L. Bowen, Jiangbing Xu, Jillian F. Banfield, Jody W. Deming, Joel E. Kostka, John M. Gladden, Josephine Z. Rapp, Joshua Sharpe, Katherine D. McMahon, Kathleen K. Treseder, Kay D. Bidle, Kelly C. Wrighton, Kimberlee Thamatrakoln, Klaus Nusslein, Laura K. Meredith, Lucia Ramirez, Marc Buee, Marcel Huntemann, Marina G. Kalyuzhnaya, Mark P. Waldrop, Matthew B. Sullivan, Matthew O. Schrenk, Matthias Hess, Michael A. Vega, Michelle A. O’Malley, Monica Medina, Naomi E. Gilbert, Nathalie Delherbe, Olivia U. Mason, Paul Dijkstra, Peter F. Chuckran, Petr Baldrian, Philippe Constant, Ramunas Stepanauskas, Rebecca A. Daly, Regina Lamendella, Robert J. Gruninger, Robert M. McKay, Samuel Hylander, Sarah L. Lebeis, Sarah P. Esser, Silvia G. Acinas, Steven S. Wilhelm, Steven W. Singer, Susannah S. Tringe, Tanja Woyke, T.B.K. Reddy, Terrence H. Bell, Thomas Mock, Tim McAllister, Vera Thiel, Vincent J. Denef, Wen-Tso Liu, Willm Martens-Habbena, Xiao-Jun Allen Liu, Zachary S. Cooper, Zhong Wang, Tel Aviv University (TAU), National Center for Biotechnology Information (NCBI), Lawrence Berkeley National Laboratory [Berkeley] (LBNL), University of Oxford, Vilnius University [Vilnius], J. Craig Venter Institute [La Jolla, USA] (JCVI), Pennsylvania State University (Penn State), Penn State System, Carnegie Institution for Science, Virologie des archées - Archaeal Virology, Université Paris Cité (UPCité)-Microbiologie Intégrative et Moléculaire (UMR6047), Institut Pasteur [Paris] (IP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Institut Pasteur [Paris] (IP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Oregon State University (OSU), U.G. and U.N. are supported by the European Research Council (ERC-AdG 787514). U.N. is supported by a fellowship from the Edmond J. Safra Center for Bioinformatics at Tel Aviv University. Y.I.W. and E.V.K. are supported through the Intramural Research Program of the US National Institutes of Health (National Library of Medicine). V.V.D. was partially supported by NIH/NLM/NCBI Visiting Scientist Fellowship. The work of the U.S. Department of Energy Joint Genome Institute (S.R., A.P.C., I.M.C., N.I., D.P.-E., N.C.K., and all JGI co-authors), a DOE Office of Science User Facility, is supported by the Office of Science of the U.S. Department of Energy under contract no. DE-AC02-05CH11231. M.K. was supported by l’Agence Nationale de la Recherche grants ANR-20-CE20-009-02 and ANR-21-CE11-0001-01. D.K. was funded by the European Social Fund under no. 09.3.3-LMT-K-712-14-0027. D.A.B. is supported by grant NNX16SJ62G from the NASA Exobiology program, and by grant DE-FG02-94ER20137 from the Photosynthetic Systems Program, Division of Chemical Sciences, Geosciences, and Biosciences (CSGB), Office of Basic Energy Sciences of the U.S. Department of Energy. We gratefully acknowledge the contributions of many scientists and principal investigators, who sent extracted genetic material for isolate genomes, environmental metagenomes, and metatranscriptomes, or sequencing results as part of the Department of Energy Joint Genome Institute Community Science Program and allowed us to include in our study the RNA virus sequences detected in these publicly available data sets regardless of publication status., The RNA Virus Discovery Consortium members are Adrienne B. Narrowe, Alexander J. Probst, Alexander Sczyrba, Annegret Kohler, Armand Séguin, Ashley Shade, Barbara J. Campbell, Björn D. Lindahl, Brandi Kiel Reese, Breanna M. Roque, Chris DeRito, Colin Averill, Daniel Cullen, David A.C. Beck, David A. Walsh, David M. Ward, Dongying Wu, Emiley Eloe-Fadrosh, Eoin L. Brodie, Erica B. Young, Erik A. Lilleskov, Federico J. Castillo, Francis M. Martin, Gary R. LeCleir, Graeme T. Attwood, Hinsby Cadillo-Quiroz, Holly M. Simon, Ian Hewson, Igor V. Grigoriev, James M. Tiedje, Janet K. Jansson, Janey Lee, Jean S. VanderGheynst, Jeff Dangl, Jeff S. Bowman, Jeffrey L. Blanchard, Jennifer L. Bowen, Jiangbing Xu, Jillian F. Banfield, Jody W. Deming, Joel E. Kostka, John M. Gladden, Josephine Z. Rapp, Joshua Sharpe, Katherine D. McMahon, Kathleen K. Treseder, Kay D. Bidle, Kelly C. Wrighton, Kimberlee Thamatrakoln, Klaus Nusslein, Laura K. Meredith, Lucia Ramirez, Marc Buee, Marcel Huntemann, Marina G. Kalyuzhnaya, Mark P. Waldrop, Matthew B. Sullivan, Matthew O. Schrenk, Matthias Hess, Michael A. Vega, Michelle A. O’Malley, Monica Medina, Naomi E. Gilbert, Nathalie Delherbe, Olivia U. Mason, Paul Dijkstra, Peter F. Chuckran, Petr Baldrian, Philippe Constant, Ramunas Stepanauskas, Rebecca A. Daly, Regina Lamendella, Robert J. Gruninger, Robert M. McKay, Samuel Hylander, Sarah L. Lebeis, Sarah P. Esser, Silvia G. Acinas, Steven S. Wilhelm, Steven W. Singer, Susannah S. Tringe, Tanja Woyke, T.B.K. Reddy, Terrence H. Bell, Thomas Mock, Tim McAllister, Vera Thiel, Vincent J. Denef, Wen-Tso Liu, Willm Martens-Habbena, Xiao-Jun Allen Liu, Zachary S. Cooper, and Zhong Wang, ANR-20-CE20-0009,VIROMET,Devoiler le virome des archées methanogenes(2020), and ANR-21-CE11-0001,ArcFus,Protéines de classe II de fusion membranaire chez les archées(2021)

Subjects
Bactriophage, RNA Virus, Virome, Functional protein annotation, Metatranscriptomics, RNA dependent RNA polymerase, Viral Ecology, viral phylogeny, Virus, Virus - Host prediction, DNA-Directed RNA Polymerases, Genome, Viral, RNA-Dependent RNA Polymerase, General Biochemistry, Genetics and Molecular Biology, [SDV.MP.VIR]Life Sciences [q-bio]/Microbiology and Parasitology/Virology, RNA, RNA Viruses, Bacteriophages, Phylogeny
Abstract

International audience; High-throughput RNA sequencing offers broad opportunities to explore the Earth RNA virome. Mining 5,150 diverse metatranscriptomes uncovered >2.5 million RNA virus contigs. Analysis of >330,000 RNA-dependent RNA polymerases (RdRPs) shows that this expansion corresponds to a 5-fold increase of the known RNA virus diversity. Gene content analysis revealed multiple protein domains previously not found in RNA viruses and implicated in virus-host interactions. Extended RdRP phylogeny supports the monophyly of the five established phyla and reveals two putative additional bacteriophage phyla and numerous putative additional classes and orders. The dramatically expanded phylum Lenarviricota, consisting of bacterial and related eukaryotic viruses, now accounts for a third of the RNA virome. Identification of CRISPR spacer matches and bacteriolytic proteins suggests that subsets of picobirnaviruses and partitiviruses, previously associated with eukaryotes, infect prokaryotic hosts.

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