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cs.CL2024
Understanding Reference Policies in Direct Preference Optimization
Yixin Liu, Pengfei Liu, Arman Cohan
Direct Preference Optimization (DPO) has become a widely used training method for the instruction fine-tuning of large language models (LLMs). In this work, we explore an under-inv…
cs.CL2024★ 2 cited
Evaluating Mathematical Reasoning Beyond Accuracy
Shijie Xia, Xuefeng Li, Yixin Liu +2
The leaderboard of Large Language Models (LLMs) in mathematical tasks has been continuously updated. However, the majority of evaluations focus solely on the final results, neglect…
cs.CL2024
Can Large Language Models be Trusted for Evaluation? Scalable Meta-Evaluation of LLMs as Evaluators via Agent Debate
Steffi Chern, Ethan Chern, Graham Neubig +1
Despite the utility of Large Language Models (LLMs) across a wide range of tasks and scenarios, developing a method for reliably evaluating LLMs across varied contexts continues to…