Papers

Learn more about AI2's Lasting Impact Award
Viewing 1-10 of 221 papers
  • Improving Language Models with Advantage-based Offline Policy Gradients

    Ashutosh Baheti, Ximing Lu, Faeze Brahman, Ronan Le Bras, Maarten Sap, Mark O. RiedlICLR2024 Language Models (LMs) achieve substantial language capabilities when finetuned using Reinforcement Learning with Human Feedback (RLHF). However, RLHF is an unstable and data-hungry process that continually requires new high-quality LM-generated data for…
  • OLMo: Accelerating the Science of Language Models

    Dirk Groeneveld, Iz Beltagy, Pete Walsh, Akshita Bhagia, Rodney Kinney, Oyvind Tafjord, A. Jha, Hamish Ivison, Ian Magnusson, Yizhong Wang, Shane Arora, David Atkinson, Russell Authur, Khyathi Raghavi Chandu, Arman Cohan, Jennifer Dumas, Yanai Elazar, Yuling Gu, Jack Hessel, Tushar Khot, William Merrill, Jacob Daniel Morrison, Niklas Muennighoff, Aakanksha Naik, Crystal Nam, Matthew E. Peters, Valentina Pyatkin, Abhilasha Ravichander, Dustin Schwenk, Saurabh Shah, Will Smith, Emma Strubell, Nishant Subramani, Mitchell Wortsman, Pradeep Dasigi, Nathan Lambert, Kyle Richardson, Luke Zettlemoyer, Jesse Dodge, Kyle Lo, Luca Soldaini, Noah A. Smith, Hanna HajishirziarXiv2024 Language models (LMs) have become ubiquitous in both NLP research and in commercial product offerings. As their commercial importance has surged, the most powerful models have become closed off, gated behind proprietary interfaces, with important details of…
  • Self-Refine: Iterative Refinement with Self-Feedback

    Aman Madaan, Niket Tandon, Prakhar Gupta, Skyler Hallinan, Luyu Gao, Sarah Wiegreffe, Uri Alon, Nouha Dziri, Shrimai Prabhumoye, Yiming Yang, Shashank Gupta, Bodhisattwa Prasad Majumder, K. Hermann, S. Welleck, A. Yazdanbakhsh, Peter ClarkNeurIPS2023 Like humans, large language models (LLMs) do not always generate the best output on their first try. Motivated by how humans refine their written text, we introduce Self-Refine, an approach for improving initial outputs from LLMs through iterative feedback…
  • Faith and Fate: Limits of Transformers on Compositionality

    Nouha Dziri, Ximing Lu, Melanie Sclar, Xiang Lorraine Li, Liwei Jian, Bill Yuchen Lin, Peter West, Chandra Bhagavatula, Ronan Le Bras, Jena D. Hwang, Soumya Sanyal, S. Welleck, Xiang Ren, Allyson Ettinger, Zaïd Harchaoui, Yejin ChoiNeurIPS2023 Transformer large language models (LLMs) have sparked admiration for their exceptional performance on tasks that demand intricate multi-step reasoning. Yet, these models simultaneously show failures on surprisingly trivial problems. This begs the question…
  • Fine-Grained Human Feedback Gives Better Rewards for Language Model Training

    Zeqiu Wu, Yushi Hu, Weijia Shi, Nouha Dziri, Alane Suhr, Prithviraj Ammanabrolu, Noah A. Smith, Mari Ostendorf, Hanna HajishirziNeurIPS2023 Language models (LMs) often exhibit undesirable text generation behaviors, including generating false, toxic, or irrelevant outputs. Reinforcement learning from human feedback (RLHF) - where human preference judgments on LM outputs are transformed into a…
  • How Far Can Camels Go? Exploring the State of Instruction Tuning on Open Resources

    Yizhong Wang, Hamish Ivison, Pradeep Dasigi, Jack Hessel, Tushar Khot, Khyathi Raghavi Chandu, David Wadden, Kelsey MacMillan, Noah A. Smith, Iz Beltagy, Hanna HajishirziNeurIPS2023 In this work we explore recent advances in instruction-tuning language models on a range of open instruction-following datasets. Despite recent claims that open models can be on par with state-of-the-art proprietary models, these claims are often accompanied…
  • RealTime QA: What's the Answer Right Now?

    Jungo Kasai, Keisuke Sakaguchi, Yoichi Takahashi, Ronan Le Bras, Akari Asai, Xinyan Velocity Yu, Dragomir R. Radev, Noah A. Smith, Yejin Choi, Kentaro InuiNeurIPS2023 We introduce R EAL T IME QA, a dynamic question answering (QA) platform that announces questions and evaluates systems on a regular basis (weekly in this version). R E AL T IME QA inquires about the current world, and QA systems need to answer questions about…
  • SwiftSage: A Generative Agent with Fast and Slow Thinking for Complex Interactive Tasks

    Bill Yuchen Lin, Yicheng Fu, Karina Yang, Prithviraj Ammanabrolu, Faeze Brahman, Shiyu Huang, Chandra Bhagavatula, Yejin Choi, Xiang RenNeurIPS2023 We introduce SwiftSage, a novel agent framework inspired by the dual-process theory of human cognition, designed to excel in action planning for complex interactive reasoning tasks. SwiftSage integrates the strengths of behavior cloning and prompting large…
  • 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…