Papers

Learn more about AI2's Lasting Impact Award
Viewing 11-20 of 950 papers
  • Editing Common Sense in Transformers

    Anshita Gupta*, Debanjan Mondal*, Akshay Krishna Sheshadri*, Wenlong Zhao, Xiang Lorraine Li*, Sarah Wiegreffe*, Niket Tandon*EMNLP2023 Editing model parameters directly in Transformers makes updating open-source transformer-based models possible without re-training. However, these editing methods have only been evaluated on statements about encyclopedic knowledge with a single correct answer…
  • FANToM: A Benchmark for Stress-testing Machine Theory of Mind in Interactions

    Hyunwoo Kim, Melanie Sclar, Xuhui Zhou, R. L. Bras, Gunhee Kim, Yejin Choi, Maarten SapEMNLP2023 Theory of mind (ToM) evaluations currently focus on testing models using passive narratives that inherently lack interactivity. We introduce FANToM, a new benchmark designed to stress-test ToM within information-asymmetric conversational contexts via question…
  • Increasing Probability Mass on Answer Choices Does Not Always Improve Accuracy

    Sarah Wiegreffe, Matthew Finlayson, Oyvind Tafjord, Peter Clark, Ashish SabharwalEMNLP2023 When pretrained language models (LMs) are applied to discriminative tasks such as multiple-choice questions, they place probability mass on vocabulary tokens that aren't among the given answer choices. Spreading probability mass across multiple surface forms…
  • Inference-Time Policy Adapters (IPA): Tailoring Extreme-Scale LMs without Fine-tuning

    Ximing Lu, Faeze Brahman, Peter West, Jaehun Jang, Khyathi Raghavi Chandu, Abhilasha Ravichander, Lianhui Qin, Prithviraj Ammanabrolu, Liwei Jiang, Sahana Ramnath, Nouha Dziri, Jillian R. Fisher, Bill Yuchen Lin, Skyler Hallinan, Xiang Ren, S. Welleck, Yejin ChoiEMNLP2023 Large language models excel at a variety of language tasks when prompted with examples or instructions. Yet controlling these models through prompting alone is limited. Tailoring language models through fine-tuning (e.g., via reinforcement learning) can be…
  • Language Models with Rationality

    Nora Kassner, Oyvind Tafjord, Ashish Sabharwal, Kyle Richardson, Hinrich Schütze, Peter ClarkEMNLP2023 While large language models (LLMs) are proficient at question-answering (QA), the dependencies between their answers and other "beliefs" they may have about the world are typically unstated, and may even be in conflict. Our goal is to uncover such…
  • Measuring and Narrowing the Compositionality Gap in Language Models

    Ofir Press, Muru Zhang, Sewon Min, Ludwig Schmidt, Noah A. Smith, Mike LewisEMNLP Findings2023 We investigate the ability of language models to perform compositional reasoning tasks where the overall solution depends on correctly composing the answers to sub-problems. We measure how often models can correctly answer all sub-problems but not generate…
  • Vera: A General-Purpose Plausibility Estimation Model for Commonsense Statements

    Jiacheng Liu, Wenya Wang, Dianzhuo Wang, Noah A. Smith, Yejin Choi, Hanna HajishirziEMNLP2023 Despite the much discussed capabilities of today's language models, they are still prone to silly and unexpected commonsense failures. We consider a retrospective verification approach that reflects on the correctness of LM outputs, and introduce Vera, a…
  • We're Afraid Language Models Aren't Modeling Ambiguity

    Alisa Liu, Zhaofeng Wu, Julian Michael, Alane Suhr, Peter West, Alexander Koller, Swabha Swayamdipta, Noah A. Smith, Yejin ChoiEMNLP2023 Ambiguity is an intrinsic feature of natural language. Managing ambiguity is a key part of human language understanding, allowing us to anticipate misunderstanding as communicators and revise our interpretations as listeners. As language models (LMs) are…
  • The Alignment Ceiling: Objective Mismatch in Reinforcement Learning from Human Feedback

    Nathan Lambert, Roberto CalandraarXiv2023 Reinforcement learning from human feedback (RLHF) has emerged as a powerful technique to make large language models (LLMs) easier to prompt and more capable in complex settings. RLHF at its core is providing a new toolkit to optimize LLMs other than next…
  • What's In My Big Data?

    Yanai Elazar, Akshita Bhagia, Ian Magnusson, Abhilasha Ravichander, Dustin Schwenk, Alane Suhr, Pete Walsh, Dirk Groeneveld, Luca Soldaini, Sameer Singh, Hanna Hajishirzi, Noah A. Smith, Jesse DodgearXiv.org2023 Large text corpora are the backbone of language models. However, we have a limited understanding of the content of these corpora, including general statistics, quality, social factors, and inclusion of evaluation data (contamination). In this work, we propose…