38 citations · 164 across the 13 of their papers we have counts for
4 papers · 1 filter
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…
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…
Take a Step Back: Evoking Reasoning via Abstraction in Large Language Models
Huaixiu Steven Zheng, Swaroop Mishra, Xinyun Chen +4
We present Step-Back Prompting, a simple prompting technique that enables LLMs to do abstractions to derive high-level concepts and first principles from instances containing speci…
Large Language Models as Analogical Reasoners
Michihiro Yasunaga, Xinyun Chen, Yujia Li +5
Chain-of-thought (CoT) prompting for language models demonstrates impressive performance across reasoning tasks, but typically needs labeled exemplars of the reasoning process. In…