Papers by Anna Sotnikova
“Flex Tape Can’t Fix That”: Bias and Misinformation in Edited Language Models (2024.emnlp-main)
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| Challenge: | Weight-based model editing methods can unintentionally alter unrelated parametric knowledge representations, potentially increasing the risk of harm. |
| Approach: | They propose a benchmark dataset for measuring bias amplification of model editing methods for demographic traits such as race, geographic origin, and gender. |
| Outcome: | The proposed methods can unintentionally alter unrelated parametric knowledge representations, potentially increasing the risk of harm. |
Theory-Grounded Measurement of U.S. Social Stereotypes in English Language Models (2022.naacl-main)
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| Challenge: | Pre-trained language models encode correlations between social groups and traits, like associating the group with the group. |
| Approach: | They adapt the Agency-Belief-Communion (ABC) stereotype model to a language model and introduce the sensitivity test (SeT) to measure stereotypical associations. |
| Outcome: | The proposed framework is used to measure stereotyping of intersectional identities in language models. |
Analyzing Stereotypes in Generative Text Inference Tasks (2021.findings-acl)
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| Challenge: | Social psychology studies how social stereotypes are shared as part of cultural knowledge . |
| Approach: | They study how stereotypes manifest when potential targets are situated in neutral contexts . they collect human judgments on the presence of stereotypes in generated inferences based on annotator positionality . |
| Outcome: | The results show that the annotators' positions differ depending on the type of inferences they generate . |
Which Examples Should be Multiply Annotated? Active Learning When Annotators May Disagree (2023.findings-acl)
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| Challenge: | Disagreement in annotations is natural for humans, depending on background, identity, positionality . many active learning approaches focus on examples where model entropy and annotator entropicy are the most different. |
| Approach: | They propose an active learning approach that focuses annotations on examples where model entropy and annotator entropic are the most different. |
| Outcome: | The proposed approach reduces the number of annotations required by 24% on average across datasets. |
Apertus: Democratizing Open and Compliant LLMs for Global Language Environments (2026.acl-long)
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Alejandro Hernández-Cano, Alexander Hägele, Allen Hao Huang, Angelika Romanou, Antoni-Joan Solergibert, Barna Pásztor, Bettina Messmer, Dhia Garbaya, Eduard Frank Ďurech, Ido Hakimi, Juan Garcia Giraldo, Mete Ismayilzada, Negar Foroutan, Skander Moalla, Tiancheng Chen, Vinko Sabolčec, Yixuan Xu, Michael Aerni, Badr AlKhamissi, Inés Altemir Marinas, Mohammad Hossein Amani, Matin Ansaripour, Ilia Badanin, Harold Benoit, Emanuela Boros, Nicholas John Browning, Fabian Bösch, Maximilian Böther, Niklas Canova, Camille Challier, Clément Charmillot, Jonathan Coles, Jan Milan Deriu, Arnout Devos, Lukas Drescher, Daniil Dzenhaliou, Maud Ehrmann, Dongyang Fan, Simin Fan, Silin Gao, Miguel Gila, María Grandury, Diba Hashemi, Alexander Miserlis Hoyle, Jiaming Jiang, Mark Klein, Andrei Kucharavy, Anastasiia Kucherenko, Frederike Lübeck, Roman Machacek, Theofilos Ioannis Manitaras, Andreas Marfurt, Kyle Matoba, Simon Matrenok, Henrique Mendonça, Fawzi Roberto Mohamed, Syrielle Montariol, Luca Mouchel, Sven Najem-Meyer, Jingwei Ni, Gennaro Oliva, Matteo Pagliardini, Elia Palme, Andrei Panferov, Léo Paoletti, Marco Passerini, Ivan Pavlov, Auguste Poiroux, Kaustubh Ponkshe, Nathan Ranchin, Javier Rando, Mathieu Sauser, Jakhongir Saydaliev, Mukhammadali Sayfiddinov, Marian Schneider, Stefano Schuppli, Marco Scialanga, Andrei Semenov, Kumar Shridhar, Raghav Singhal, Anna Sotnikova, Alexander Sternfeld, Ayush Kumar Tarun, Paul Teiletche, Jannis Vamvas, Xiaozhe Yao, Hao Zhao, Alexander Ilic, Ana Klimovic, Andreas Krause, Caglar Gulcehre, David Rosenthal, Elliott Ash, Florian Tramèr, Joost VandeVondele, Livio Veraldi, Martin Rajman, Thomas C. Schulthess, Torsten Hoefler, Antoine Bosselut, Martin Jaggi, Imanol Schlag
| Challenge: | Apertus is a fully open suite of large language models (LLMs) designed to address responsibility shortcomings in today’s open model ecosystem, namely data responsibility and global representation. |
| Approach: | They propose to release a fully open suite of large language models (LLMs) that address data responsibility and global representation shortcomings in today’s open model ecosystem. |
| Outcome: | The proposed model is pretrained on openly available data and suppresses verbatim recall of data while retaining task performance. |