Papers

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Viewing 21-30 of 212 papers
  • Exploiting Generalization in Offline Reinforcement Learning via Unseen State Augmentations

    Nirbhay Modhe, Qiaozi Gao, A. Kalyan, Dhruv Batra, G. Thattai, G. SukhatmearXiv.org2023 Offline reinforcement learning (RL) methods strike a balance between exploration and exploitation by conservative value estimation -- penalizing values of unseen states and actions. Model-free methods penalize values at all unseen actions, while model-based…
  • DISCO: Distilling Phrasal Counterfactuals with Large Language Models

    Zeming Chen, Qiyue Gao, Kyle Richardson, Antoine Bosselut, Ashish SabharwalACL2023 Recent methods demonstrate that data augmentation using counterfactual knowledge can teach models the causal structure of a task, leading to robust and generalizable models. However, such counterfactual data often has a limited scale and diversity if…
  • Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questions

    Harsh Trivedi, Niranjan Balasubramanian, Tushar Khot, Ashish SabharwalACL2023 Prompting-based large language models (LLMs) are surprisingly powerful at generating natural language reasoning steps or Chains-of-Thoughts (CoT) for multi-step question answering (QA). They struggle, however, when the necessary knowledge is either…
  • Do language models have coherent mental models of everyday things?

    Yuling Gu, Bhavana Dalvi Mishra, Peter ClarkACL2023 When people think of everyday things like an “egg,” they typically have a mental image associated with it. This commonsense knowledge helps us understand how these everyday things work and how to interact with them. For example, when someone tries to make a…
  • RL4F: Generating Natural Language Feedback with Reinforcement Learning for Repairing Model Outputs

    Afra Feyza Akyurek, Ekin Akyürek, Aman Madaan, A. Kalyan, Peter Clark, D. Wijaya, Niket TandonAnnual Meeting of the Association for Computational Linguistics2023 Despite their unprecedented success, even the largest language models make mistakes.Similar to how humans learn and improve using feedback, previous work proposed providing language models with natural language feedback to guide them in repairing their…
  • Let Me Teach You: Pedagogical Foundations of Feedback for Language Models

    Beatriz Borges, Niket Tandon, Tanja Kaser, Antoine BosselutarXiv2023 Natural Language Feedback (NLF) is an increasingly popular avenue to align Large Language Models (LLMs) to human preferences. Despite the richness and diversity of the information it can convey, NLF is often hand-designed and arbitrary. In a different world…
  • Chain-of-Thought Hub: A Continuous Effort to Measure Large Language Models' Reasoning Performance

    Yao Fu, Litu Ou, Mingyu Chen, Yuhao Wan, Hao Peng, Tushar KhotICML 2023, the Challenges in Deployable Generative AI workshop2023 As large language models (LLMs) are continuously being developed, their evaluation becomes increasingly important yet challenging. This work proposes Chain-of-Thought Hub, an open-source evaluation suite on the multi-step reasoning capabilities of large…
  • The Tail Wagging the Dog: Dataset Construction Biases of Social Bias Benchmarks

    Nikil Selvam, Sunipa Dev, Daniel Khashabi, Tushar Khot, Kai-Wei ChangACL2023 How reliably can we trust the scores obtained from social bias benchmarks as faithful indicators of problematic social biases in a given language model? In this work, we study this question by contrasting social biases with non-social biases stemming from…
  • Aligning Language Models to User Opinions

    EunJeong Hwang, Bodhisattwa Prasad Majumder, Niket TandonarXiv2023 An important aspect of developing LLMs that interact with humans is to align models' behavior to their users. It is possible to prompt an LLM into behaving as a certain persona, especially a user group or ideological persona the model captured during its…
  • Anthropomorphization of AI: Opportunities and Risks

    A. Deshpande, Tanmay Rajpurohit, Karthik Narasimhan, A. KalyanarXiv.org2023 Anthropomorphization is the tendency to attribute human-like traits to non-human entities. It is prevalent in many social contexts -- children anthropomorphize toys, adults do so with brands, and it is a literary device. It is also a versatile tool in science…