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Decomposed Prompting: A Modular Approach for Solving Complex Tasks
Tushar Khot, Harsh Trivedi, Matthew Finlayson, Yao Fu, Kyle Richardson, Peter Clark, Ashish SabharwalICLR • 2023 Few-shot prompting is a surprisingly powerful way to use Large Language Models (LLMs) to solve various tasks. However, this approach struggles as the task complexity increases or when the individual reasoning steps of the task themselves are hard to learn…Toxicity in ChatGPT: Analyzing Persona-assigned Language Models
A. Deshpande, Vishvak Murahari, Tanmay Rajpurohit, A. Kalyan, Karthik NarasimhanarXiv.org • 2023 Large language models (LLMs) have shown incredible capabilities and transcended the natural language processing (NLP) community, with adoption throughout many services like healthcare, therapy, education, and customer service. Since users include people with…The Parallelism Tradeoff: Limitations of Log-Precision Transformers
William Merrill, Ashish SabharwalTACL • ACL • 2023 Abstract Despite their omnipresence in modern NLP, characterizing the computational power of transformer neural nets remains an interesting open question. We prove that transformers whose arithmetic precision is logarithmic in the number of input tokens (and…Specializing Smaller Language Models towards Multi-Step Reasoning
Yao Fu, Hao Peng, Litu Ou, Ashish Sabharwal, Tushar KhotICML • 2023 The surprising ability of Large Language Models (LLMs) to perform well on complex reasoning with only few-shot chain-of-thought prompts is believed to emerge only in very large-scale models (100+ billion parameters). We show that such abilities can, in fact…ProKnow: Process knowledge for safety constrained and explainable question generation for mental health diagnostic assistance
Kaushik Roy, Manas Gaur, Misagh Soltani, Vipula Rawte, A. Kalyan, Amit P. ShethFrontiers in Big Data • 2023 Virtual Mental Health Assistants (VMHAs) are utilized in health care to provide patient services such as counseling and suggestive care. They are not used for patient diagnostic assistance because they cannot adhere to safety constraints and specialized…I Cast Detect Thoughts: Learning to Converse and Guide with Intents and Theory-of-Mind in Dungeons and Dragons
Pei Zhou, Andrew Zhu, Jennifer Hu, J. Pujara, Xiang Ren, Chris Callison-Burch, Yejin Choi, Prithviraj AmmanabroluarXiv • 2022 We propose a novel task, G4C, to study teacher-student natural language interactions in a goal-driven and grounded environment. Dungeons and Dragons (D&D), a role-playing game, provides an ideal setting to investigate such interactions. Here, the Dungeon…Lila: A Unified Benchmark for Mathematical Reasoning
Swaroop Mishra, Matthew Finlayson, Pan Lu, Leonard Tang, Sean Welleck, Chitta Baral, Tanmay Rajpurohit, Oyvind Tafjord, Ashish Sabharwal, Peter Clark, Ashwin KalyanEMNLP • 2022 Mathematical reasoning skills are essential for general-purpose intelligent systems to perform tasks from grocery shopping to climate modeling. Towards evaluating and improving AI systems in this domain, we propose LILA, a unified mathematical reasoning…Entailer: Answering Questions with Faithful and Truthful Chains of Reasoning
Oyvind Tafjord, Bhavana Dalvi Mishra, Peter ClarkEMNLP • 2022 Our goal is a question-answering (QA) system that can show how its answers are implied by its own internal beliefs via a systematic chain of reasoning . Such a capability would allow better understanding of why a model produced the answer it did. Our approach…Teaching Broad Reasoning Skills via Decomposition-Guided Contexts
Harsh Trivedi, Niranjan Balasubramanian, Tushar Khot, Ashish SabharwalEMNLP • 2022 Question-answering datasets require a broad set of reasoning skills. We show how to use question decompositions to teach language models these broad reasoning skills in a robust fashion. Specifically, we use widely available QDMR representations to…Towards Teachable Reasoning Systems: Using a Dynamic Memory of User Feedback for Continual System Improvement
Bhavana Dalvi Mishra, Oyvind Tafjord, Peter ClarkEMNLP • 2022 Our goal is a teachable reasoning system for question-answering (QA), where a user can interact with faithful answer explanations, and correct its errors so that the system improves over time. Our approach is to augment a QA model with a dynamic memory of…