96 citations · 209 across the 11 of their papers we have counts for
4 papers · 1 filter
Better and Worse with Scale: How Contextual Entrainment Diverges with Model Size
Dikshant Kukreja, Kshitij Sah, Gautam Gupta +5
Larger language models become simultaneously better and worse at handling contextual information -- better at ignoring false claims, worse at ignoring irrelevant tokens. We formali…
NATURAL PLAN: Benchmarking LLMs on Natural Language Planning
Huaixiu Steven Zheng, Swaroop Mishra, Hugh Zhang +8
We introduce NATURAL PLAN, a realistic planning benchmark in natural language containing 3 key tasks: Trip Planning, Meeting Planning, and Calendar Scheduling. We focus our evaluat…
Universal Self-Consistency for Large Language Model Generation
Xinyun Chen, Renat Aksitov, Uri Alon +7
Self-consistency with chain-of-thought prompting (CoT) has demonstrated remarkable performance gains on various challenging tasks, by utilizing multiple reasoning paths sampled fro…
Large Language Models Cannot Self-Correct Reasoning Yet
Jie Huang, Xinyun Chen, Swaroop Mishra +4
Large Language Models (LLMs) have emerged as a groundbreaking technology with their unparalleled text generation capabilities across various applications. Nevertheless, concerns pe…