38 citations · 164 across the 13 of their papers we have counts for
6 papers · 1 filter
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…
Transformers Can Achieve Length Generalization But Not Robustly
Yongchao Zhou, Uri Alon, Xinyun Chen +3
Length generalization, defined as the ability to extrapolate from shorter training sequences to longer test ones, is a significant challenge for language models. This issue persist…
Self-Discover: Large Language Models Self-Compose Reasoning Structures
Pei Zhou, Jay Pujara, Xiang Ren +7
We introduce SELF-DISCOVER, a general framework for LLMs to self-discover the task-intrinsic reasoning structures to tackle complex reasoning problems that are challenging for typi…
Chain of Thought Empowers Transformers to Solve Inherently Serial Problems
Zhiyuan Li, Hong Liu, Denny Zhou +1
Instructing the model to generate a sequence of intermediate steps, a.k.a., a chain of thought (CoT), is a highly effective method to improve the accuracy of large language models…
Chain-of-Thought Reasoning Without Prompting
Xuezhi Wang, Denny Zhou
In enhancing the reasoning capabilities of large language models (LLMs), prior research primarily focuses on specific prompting techniques such as few-shot or zero-shot chain-of-th…
Premise Order Matters in Reasoning with Large Language Models
Xinyun Chen, Ryan A. Chi, Xuezhi Wang +1
Large language models (LLMs) have accomplished remarkable reasoning performance in various domains. However, in the domain of reasoning tasks, we discover a frailty: LLMs are surpr…