2 papers
cs.CL2024
Can LLMs Learn from Previous Mistakes? Investigating LLMs' Errors to Boost for Reasoning
Yongqi Tong, Dawei Li, Sizhe Wang +3
Recent works have shown the benefits to LLMs from fine-tuning golden-standard Chain-of-Thought (CoT) rationales or using them as correct examples in few-shot prompting. While human…
cs.CL2024
Optimizing Language Model's Reasoning Abilities with Weak Supervision
Yongqi Tong, Sizhe Wang, Dawei Li +6
While Large Language Models (LLMs) have demonstrated proficiency in handling complex queries, much of the past work has depended on extensively annotated datasets by human experts.…