100 citations · 139 across the 20 of their papers we have counts for
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Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questions
Harsh Trivedi, Niranjan Balasubramanian, Tushar Khot +1
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…
Two-Turn Debate Doesn't Help Humans Answer Hard Reading Comprehension Questions
Alicia Parrish, Harsh Trivedi, Nikita Nangia +4
The use of language-model-based question-answering systems to aid humans in completing difficult tasks is limited, in part, by the unreliability of the text these systems generate.…
Decomposed Prompting: A Modular Approach for Solving Complex Tasks
Tushar Khot, Harsh Trivedi, Matthew Finlayson +4
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 w…
Teaching Broad Reasoning Skills for Multi-Step QA by Generating Hard Contexts
Harsh Trivedi, Niranjan Balasubramanian, Tushar Khot +1
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 fa…
Single-Turn Debate Does Not Help Humans Answer Hard Reading-Comprehension Questions
Alicia Parrish, Harsh Trivedi, Ethan Perez +4
Current QA systems can generate reasonable-sounding yet false answers without explanation or evidence for the generated answer, which is especially problematic when humans cannot r…