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1. Large Language Models as Misleading Assistants in Conversation

Author

Hou, Betty Li, Shi, Kejian, Phang, Jason, Aung, James, Adler, Steven, and Campbell, Rosie

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence, Computer Science - Computers and Society
Abstract

Large Language Models (LLMs) are able to provide assistance on a wide range of information-seeking tasks. However, model outputs may be misleading, whether unintentionally or in cases of intentional deception. We investigate the ability of LLMs to be deceptive in the context of providing assistance on a reading comprehension task, using LLMs as proxies for human users. We compare outcomes of (1) when the model is prompted to provide truthful assistance, (2) when it is prompted to be subtly misleading, and (3) when it is prompted to argue for an incorrect answer. Our experiments show that GPT-4 can effectively mislead both GPT-3.5-Turbo and GPT-4, with deceptive assistants resulting in up to a 23% drop in accuracy on the task compared to when a truthful assistant is used. We also find that providing the user model with additional context from the passage partially mitigates the influence of the deceptive model. This work highlights the ability of LLMs to produce misleading information and the effects this may have in real-world situations., Comment: Next Generation of AI Safety Workshop, 41st International Conference on Machine Learning (ICML 2024)

Published
2024

2. GPT-4 Technical Report

Author

OpenAI, Achiam, Josh, Adler, Steven, Agarwal, Sandhini, Ahmad, Lama, Akkaya, Ilge, Aleman, Florencia Leoni, Almeida, Diogo, Altenschmidt, Janko, Altman, Sam, Anadkat, Shyamal, Avila, Red, Babuschkin, Igor, Balaji, Suchir, Balcom, Valerie, Baltescu, Paul, Bao, Haiming, Bavarian, Mohammad, Belgum, Jeff, Bello, Irwan, Berdine, Jake, Bernadett-Shapiro, Gabriel, Berner, Christopher, Bogdonoff, Lenny, Boiko, Oleg, Boyd, Madelaine, Brakman, Anna-Luisa, Brockman, Greg, Brooks, Tim, Brundage, Miles, Button, Kevin, Cai, Trevor, Campbell, Rosie, Cann, Andrew, Carey, Brittany, Carlson, Chelsea, Carmichael, Rory, Chan, Brooke, Chang, Che, Chantzis, Fotis, Chen, Derek, Chen, Sully, Chen, Ruby, Chen, Jason, Chen, Mark, Chess, Ben, Cho, Chester, Chu, Casey, Chung, Hyung Won, Cummings, Dave, Currier, Jeremiah, Dai, Yunxing, Decareaux, Cory, Degry, Thomas, Deutsch, Noah, Deville, Damien, Dhar, Arka, Dohan, David, Dowling, Steve, Dunning, Sheila, Ecoffet, Adrien, Eleti, Atty, Eloundou, Tyna, Farhi, David, Fedus, Liam, Felix, Niko, Fishman, Simón Posada, Forte, Juston, Fulford, Isabella, Gao, Leo, Georges, Elie, Gibson, Christian, Goel, Vik, Gogineni, Tarun, Goh, Gabriel, Gontijo-Lopes, Rapha, Gordon, Jonathan, Grafstein, Morgan, Gray, Scott, Greene, Ryan, Gross, Joshua, Gu, Shixiang Shane, Guo, Yufei, Hallacy, Chris, Han, Jesse, Harris, Jeff, He, Yuchen, Heaton, Mike, Heidecke, Johannes, Hesse, Chris, Hickey, Alan, Hickey, Wade, Hoeschele, Peter, Houghton, Brandon, Hsu, Kenny, Hu, Shengli, Hu, Xin, Huizinga, Joost, Jain, Shantanu, Jain, Shawn, Jang, Joanne, Jiang, Angela, Jiang, Roger, Jin, Haozhun, Jin, Denny, Jomoto, Shino, Jonn, Billie, Jun, Heewoo, Kaftan, Tomer, Kaiser, Łukasz, Kamali, Ali, Kanitscheider, Ingmar, Keskar, Nitish Shirish, Khan, Tabarak, Kilpatrick, Logan, Kim, Jong Wook, Kim, Christina, Kim, Yongjik, Kirchner, Jan Hendrik, Kiros, Jamie, Knight, Matt, Kokotajlo, Daniel, Kondraciuk, Łukasz, Kondrich, Andrew, Konstantinidis, Aris, Kosic, Kyle, Krueger, Gretchen, Kuo, Vishal, Lampe, Michael, Lan, Ikai, Lee, Teddy, Leike, Jan, Leung, Jade, Levy, Daniel, Li, Chak Ming, Lim, Rachel, Lin, Molly, Lin, Stephanie, Litwin, Mateusz, Lopez, Theresa, Lowe, Ryan, Lue, Patricia, Makanju, Anna, Malfacini, Kim, Manning, Sam, Markov, Todor, Markovski, Yaniv, Martin, Bianca, Mayer, Katie, Mayne, Andrew, McGrew, Bob, McKinney, Scott Mayer, McLeavey, Christine, McMillan, Paul, McNeil, Jake, Medina, David, Mehta, Aalok, Menick, Jacob, Metz, Luke, Mishchenko, Andrey, Mishkin, Pamela, Monaco, Vinnie, Morikawa, Evan, Mossing, Daniel, Mu, Tong, Murati, Mira, Murk, Oleg, Mély, David, Nair, Ashvin, Nakano, Reiichiro, Nayak, Rajeev, Neelakantan, Arvind, Ngo, Richard, Noh, Hyeonwoo, Ouyang, Long, O'Keefe, Cullen, Pachocki, Jakub, Paino, Alex, Palermo, Joe, Pantuliano, Ashley, Parascandolo, Giambattista, Parish, Joel, Parparita, Emy, Passos, Alex, Pavlov, Mikhail, Peng, Andrew, Perelman, Adam, Peres, Filipe de Avila Belbute, Petrov, Michael, Pinto, Henrique Ponde de Oliveira, Michael, Pokorny, Pokrass, Michelle, Pong, Vitchyr H., Powell, Tolly, Power, Alethea, Power, Boris, Proehl, Elizabeth, Puri, Raul, Radford, Alec, Rae, Jack, Ramesh, Aditya, Raymond, Cameron, Real, Francis, Rimbach, Kendra, Ross, Carl, Rotsted, Bob, Roussez, Henri, Ryder, Nick, Saltarelli, Mario, Sanders, Ted, Santurkar, Shibani, Sastry, Girish, Schmidt, Heather, Schnurr, David, Schulman, John, Selsam, Daniel, Sheppard, Kyla, Sherbakov, Toki, Shieh, Jessica, Shoker, Sarah, Shyam, Pranav, Sidor, Szymon, Sigler, Eric, Simens, Maddie, Sitkin, Jordan, Slama, Katarina, Sohl, Ian, Sokolowsky, Benjamin, Song, Yang, Staudacher, Natalie, Such, Felipe Petroski, Summers, Natalie, Sutskever, Ilya, Tang, Jie, Tezak, Nikolas, Thompson, Madeleine B., Tillet, Phil, Tootoonchian, Amin, Tseng, Elizabeth, Tuggle, Preston, Turley, Nick, Tworek, Jerry, Uribe, Juan Felipe Cerón, Vallone, Andrea, Vijayvergiya, Arun, Voss, Chelsea, Wainwright, Carroll, Wang, Justin Jay, Wang, Alvin, Wang, Ben, Ward, Jonathan, Wei, Jason, Weinmann, CJ, Welihinda, Akila, Welinder, Peter, Weng, Jiayi, Weng, Lilian, Wiethoff, Matt, Willner, Dave, Winter, Clemens, Wolrich, Samuel, Wong, Hannah, Workman, Lauren, Wu, Sherwin, Wu, Jeff, Wu, Michael, Xiao, Kai, Xu, Tao, Yoo, Sarah, Yu, Kevin, Yuan, Qiming, Zaremba, Wojciech, Zellers, Rowan, Zhang, Chong, Zhang, Marvin, Zhao, Shengjia, Zheng, Tianhao, Zhuang, Juntang, Zhuk, William, and Zoph, Barret

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence
Abstract

We report the development of GPT-4, a large-scale, multimodal model which can accept image and text inputs and produce text outputs. While less capable than humans in many real-world scenarios, GPT-4 exhibits human-level performance on various professional and academic benchmarks, including passing a simulated bar exam with a score around the top 10% of test takers. GPT-4 is a Transformer-based model pre-trained to predict the next token in a document. The post-training alignment process results in improved performance on measures of factuality and adherence to desired behavior. A core component of this project was developing infrastructure and optimization methods that behave predictably across a wide range of scales. This allowed us to accurately predict some aspects of GPT-4's performance based on models trained with no more than 1/1,000th the compute of GPT-4., Comment: 100 pages; updated authors list; fixed author names and added citation

Published
2023

15. ‘Whitby Woman’, ‘Waitrose Woman’: Gender and Voting Behaviour at the 2024 UK General Election.

Author

Campbell, Rosie, Fowler, Ceri, Sanders, Anna, and Shorrocks, Rosalind

Abstract

Women were identified as key targets in the 2024 British general election. There was much speculation as to whether ‘Whitby’ or ‘Waitrose’ women would swing the result for Labour. This interest in women voters stemmed, at least partially, from the fact that the 2017 and 2019 British general elections were the first where a modern gender gap—a greater proportion of women voting Labour than men and a greater proportion of men voting Conservative than women—was evident in the UK. This article assesses whether the parties’ attempts to target women voters were successful and whether a modern gender gap was a feature of the 2024 general election. Moving beyond analysis of the traditional two largest parties and their associated gender gaps, it is discussed how the gender gap in the UK might be changing as the party system fragments, with a smaller Labour‐Conservative gap, but a large left‐right gap persisting. [ABSTRACT FROM AUTHOR]

Published
2024
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16. Endometriosis as a risk factor for ovarian cancer: an update on screening, risk reduction, treatment and prognosis.

Author

Campbell, Rosie, Milford, Kirsty, Peyton‐Jones, Benjamin, and Hannemann, Michael

Subjects
*RISK assessment, *PUBLIC health surveillance, *SALPINGO-oophorectomy, *OVARIAN tumors, *EARLY detection of cancer, *OXIDATIVE stress, *ENDOMETRIOSIS, *CHRONIC diseases, *TUBAL sterilization, *INFLAMMATION, *GENETIC mutation, *CONTRACEPTION, *DISEASE risk factors
Abstract

Key content: Endometriosis affects up to 10% of women. While many regard endometriosis as a benign (non‐cancerous) disease, there are well‐established links to epithelial ovarian cancer (EOC).Proposed mechanisms for malignant transformation include chronic inflammation, hyperestrogenism and oxidative stress. Mutations in the tumour suppressor gene ARID1A have also been implicated in the development of endometriosis‐associated ovarian cancer (EAOC).Endometriosis is reported to almost double a woman's background risk of ovarian cancer.Risk reduction strategies are effective in reducing the incidence of EAOC. These include tubal ligation, excision of endometriosis and combined hormonal contraception.Novel therapies targeting tumour suppressor gene mutations present in EAOC are undergoing phase I & II clinical trials. Learning objectives: To increase knowledge of the association between endometriosis and EOC.To understand the updated evidence for screening, risk and risk reduction strategies.To be informed of potential novel therapies currently in the clinical trial stage of development. Ethical issues: Increased public awareness and improved clinician knowledge may lead to more and earlier‐stage disease detection.Women are more informed, necessitating clinicians to deliver evidence‐based counselling to meet their informational needs. [ABSTRACT FROM AUTHOR]

Published
2024
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22. Policing Online Sex Markets

Author

Sanders, Teela, Scoular, Jane, Campbell, Rosie, Pitcher, Jane, Cunningham, Stewart, Sanders, Teela, Scoular, Jane, Campbell, Rosie, Pitcher, Jane, and Cunningham, Stewart

Published
2018
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23. Conclusion: Key Messages

Author

Sanders, Teela, Scoular, Jane, Campbell, Rosie, Pitcher, Jane, Cunningham, Stewart, Sanders, Teela, Scoular, Jane, Campbell, Rosie, Pitcher, Jane, and Cunningham, Stewart

Published
2018
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34. England and Wales

Author

Campbell, Rosie, primary, Hubbard, Phil, additional, Sanders, Teela, additional, and Scoular, Jane, additional

Published
2019
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35. Informal settlements:Covid-19 and sex workers in Kenya

Author

Hassan, Rahma, Sanders, Teela, Gichuna, Susan, Campbell, Rosie, Mutonyi, Mercy, Mwangi, Peninah, Hassan, Rahma, Sanders, Teela, Gichuna, Susan, Campbell, Rosie, Mutonyi, Mercy, and Mwangi, Peninah

Abstract

This paper highlights the challenges faced by female sex workers living and working in the urban informal settlements in Nairobi, Kenya, during the Covid-19 outbreak and the aftermath of the pandemic. Using data collected through phone interviews during the immediate crisis, we document the experiences of urban poor sex workers, illustrating the acute problems they faced, including precarious housing with the reality of eviction and demolition. The paper highlights the ramifications of the Covid-19 crisis for the sex industry and predominantly women working within this informal, illegal economy. Through our empirical data we illustrate how the nature of selling sex has changed for sex workers in this context, increasing risks of violence including police abuses. We argue that examining the Covid-19 crisis through the lens of one the most marginalised populations graphically highlights how the pandemic has and will continue to deepen pre-existing structural urban inequalities and worsen public health outcomes among the urban poor. Sex worker communities are often located at the intersections of structural inequalities of gender, class, race and nation and the socio-spatial fragmentations of how they live make them some of the most vulnerable in society. We close with comments in relation to sexual citizenship, exclusionary state practices and the feminisation of urban poverty.

Published
2023

36. Sex Work, Hate Crime and the Criminal law

Author

Sanders, Teela, Scoular, Jane, and Campbell, Rosie

Subjects
KD, HV7231, Law
Abstract

It has been well established at a global level that sex workers are often victims of direct violence in the course of their work, targeted by their ‘perceived vulnerability’ as a marginalised group. In one police force in England (Merseyside) since 2006 they have addressed this victimisation through adopting a ‘hate crime’ approach to policing crimes against sex workers. The aims of this paper are first, to review the implementation of the hate crime model applied to crimes against sex workers; second, to explore how police forces are adopting the policy across the UK, and the operational barriers to doing so; and third to explore the legal, theoretical and critical issues raised by treating crimes against sex workers as a type of hate crime through policing models. The conclusion weighs up some of the challenges to expanding the current law to include sex workers as a target group for increased protection, assessing that a fully funded policing approach may well be sufficient alongside law reform around decriminalisation of sex work. This article will draw on data from police forces in England. We acknowledge that much more work is needed to establish the sex work community's views on the value, if any, of the hate crime approach.

Published
2022

37. Technology-mediated sex work

Author

Campbell, Rosie, primary, Aydin, Yigit, additional, Cunningham, Stewart, additional, Hamer, Rebecca, additional, Hill, Kathleen, additional, Melissa, Camille, additional, Pitcher, Jane, additional, Scoular, Jane, additional, Sanders, Teela, additional, and Valentine-Chase, Matt, additional

Published
2018
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41. Internet Sex Work

Author

Sanders, Teela, primary, Scoular, Jane, additional, Campbell, Rosie, additional, Pitcher, Jane, additional, and Cunningham, Stewart, additional

Published
2018
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44. Retaining Roots While Hard Pruning Data: Context and Collaboration in Digitisation and Data Modelling in South Asia

Author

Vafadari, Azadeh, Abdul Jabbar, Junaid, Khan, Afifa, Roberts, Rebecca C., Campbell, Rosie, Gerrits, Petrus, Gregorio, Jonas, Suarez Moreno, Maria, Tomaney, Jack, and A. Petrie, Cameron

Subjects
Data, Archaeology, FAIR
Abstract

There are large numbers of published (and unpublished) archaeological sites and monuments in South Asia, but much of the available documentation is not available in a digital format, and paper publication remains the default. Information about archaeological heritage varies in detail, and there is considerable variation in how archaeological sites are documented. This paper explores the work and challenges of the Mapping Archaeological Heritage in South Asia (MAHSA) project in digitising, modelling, re-interpreting, linking, and re-using non-digital archaeological data into a structured and standardised digital format using a common and unique controlled vocabulary, so that it may become findable, accessible, interoperable, and reusable data. Working with partners in South Asia, the MAHSA project is compiling existing and published data (from published documents, reports, and surveys) to be published in an Open Access database (Arches platform), which can be used for research and preservation. New and previously undocumented sites identified through the analysis of historic maps, remote sensing, automated site detection methods, and field documentation will also follow the same data standards and recording methodology. Retaining context while delivering ‘cleaned’ usable data is a key challenge to this type of data modelling, and the meanings of the original source and context are inevitably subject to re-interpretation during this process. The project is working collaboratively between South Asian and international stakeholders, each bringing their own expectations of data use, management, and accessibility, in addition to navigating the post-colonial space in which the project operates. To ensure usability, different use cases scenarios have been identified and are being developed with collaborators and database end-users. We argue that through linking archaeological data to their original sources and following best practices in modelling techniques, we can remain faithful to the original context while incorporating any change and enhancements where data needs to be verified and updated.

Published
2022
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49. Policing Online Sex Markets

Author

Sanders, Teela, primary, Scoular, Jane, additional, Campbell, Rosie, additional, Pitcher, Jane, additional, and Cunningham, Stewart, additional

Published
2017
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