565 citations · 1.7k across the 55 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…
The Tail Wagging the Dog: Dataset Construction Biases of Social Bias Benchmarks
Nikil Roashan Selvam, Sunipa Dev, Daniel Khashabi +2
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…
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…
Complexity-Based Prompting for Multi-Step Reasoning
Yao Fu, Hao Peng, Ashish Sabharwal +2
We study the task of prompting large-scale language models to perform multi-step reasoning. Existing work shows that when prompted with a chain of thoughts (CoT), sequences of shor…
Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models
Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao +448
Language models demonstrate both quantitative improvement and new qualitative capabilities with increasing scale. Despite their potentially transformative impact, these new capabil…
Better Retrieval May Not Lead to Better Question Answering
Zhengzhong Liang, Tushar Khot, Steven Bethard +2
Considerable progress has been made recently in open-domain question answering (QA) problems, which require Information Retrieval (IR) and Reading Comprehension (RC). A popular app…
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…