96 citations · 209 across the 11 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…
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…
Large Language Models as Optimizers
Chengrun Yang, Xuezhi Wang, Yifeng Lu +4
Optimization is ubiquitous. While derivative-based algorithms have been powerful tools for various problems, the absence of gradient imposes challenges on many real-world applicati…