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cs.CL2024
Teaching Language Models to Self-Improve by Learning from Language Feedback
Chi Hu, Yimin Hu, Hang Cao +2
Aligning Large Language Models (LLMs) with human intentions and values is crucial yet challenging. Current methods primarily rely on human preferences, which are costly and insuffi…
cs.CL2024★ 2 cited
RankPrompt: Step-by-Step Comparisons Make Language Models Better Reasoners
Chi Hu, Yuan Ge, Xiangnan Ma +5
Large Language Models (LLMs) have achieved impressive performance across various reasoning tasks. However, even state-of-the-art LLMs such as ChatGPT are prone to logical errors du…
cs.CL2024
Clustering and Ranking: Diversity-preserved Instruction Selection through Expert-aligned Quality Estimation
Yuan Ge, Yilun Liu, Chi Hu +10
With contributions from the open-source community, a vast amount of instruction tuning (IT) data has emerged. Given the significant resource allocation required for training and ev…