Papers

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Viewing 1-10 of 159 papers
  • DREAM: Improving Situational QA by First Elaborating the Situation

    Yuling Gu, Bhavana Dalvi Mishra, Peter ClarkNAACL 20212022 When people answer questions about a specific situation, e.g., "I cheated on my mid-term exam last week. Was that wrong?", cognitive science suggests that they form a mental picture of that situation before answering. While we do not know how language models…
  • Cross-Task Generalization via Natural Language Crowdsourcing Instructions

    Swaroop Mishra, Daniel Khashabi, Chitta Baral, Hanna HajishirziACL2022 Can we enable NLP models to appropriately respond to instructional prompts and consequently generalize to new tasks? To study this question, we leverage the existing NLP datasets and the instructions that were used to crowdsource them to create…
  • Hey AI, Can You Solve Complex Tasks by Talking to Agents?

    Tushar Khot, Kyle Richardson, Daniel Khashabi, Ashish SabharwalFindings of ACL2022 Humans often solve complex problems by interacting (in natural language) with existing agents, such as AI assistants, that can solve simpler sub-tasks. These agents themselves can be powerful systems built using extensive resources and privately held data. In…
  • Saturated Transformers are Constant-Depth Threshold Circuits

    William Cooper Merrill, Ashish Sabharwal, Noah A. SmithTACL2022 Transformers have become a standard neural network architecture for many NLP problems, motivating theoretical analysis of their power in terms of formal languages. Recent work has shown that transformers with *hard* attention are quite limited in power, as…
  • What Makes Instruction Learning Hard? An Investigation and a New Challenge in a Synthetic Environment

    Matthew Finlayson, Kyle Richardson, Ashish Sabharwal, Peter ClarkarXiv2022 The instruction learning paradigm—where a model learns to perform new tasks from task descriptions alone—has become popular in general-purpose model research. The capabilities of large transformer models as instruction learners, however, remain poorly under…
  • Towards Teachable Reasoning Systems

    Bhavana Dalvi, Oyvind Tafjord, Peter ClarkarXiv2022 Our goal is a teachable reasoning system for question-answering (QA), where a user can interact with faithful answer explanations, and correct errors so that the system improves over time. Our approach is three-fold: First, generated chains of reasoning show…
  • What Makes Instruction Learning Hard? An Investigation and a New Challenge in a Synthetic Environment

    Matthew Finlayson, Kyle Richardson, Ashish Sabharwal, Peter ClarkarXiv2022 The instruction learning paradigm—where a model learns to perform new tasks from task descriptions alone—has become popular in general-purpose model research. The capabilities of large transformer models as instruction learners, however, remain poorly under…
  • NumGLUE: A Suite of Fundamental yet Challenging Mathematical Reasoning Tasks

    Swaroop Mishra, Arindam Mitra, Neeraj Varshney, Bhavdeep Singh Sachdeva, Peter Clark, Chitta Baral, A. KalyanarXiv2022 Given the ubiquitous nature of numbers in text, reasoning with numbers to perform simple calculations is an important skill of AI systems. While many datasets and models have been developed to this end, state-of-the-art AI systems are brittle; failing to…
  • Memory-assisted prompt editing to improve GPT-3 after deployment

    Aman Madaan, Niket Tandon, Peter Clark, Yiming YangarXiv2022 Large LMs such as GPT-3 are powerful, but can commit mistakes that are obvious to humans. For example, GPT-3 would mistakenly interpret "What word is similar to good?" to mean a homonym, while the user intended a synonym. Our goal is to effectively correct…
  • Multi-Modal Answer Validation for Knowledge-Based VQA

    Jialin Wu, Jiasen Lu, Ashish Sabharwal, R. MottaghiAAAI2022 The problem of knowledge-based visual question answering involves answering questions that require external knowledge in addition to the content of the image. Such knowledge typically comes in a variety of forms, including visual, textual, and commonsense…