2 citations · 3 across the 3 of their papers we have counts for
3 papers
cs.AI2024★ 1 cited
-DPO: Direct Preference Optimization with Dynamic
Junkang Wu, Yuexiang Xie, Zhengyi Yang +5
Direct Preference Optimization (DPO) has emerged as a compelling approach for training Large Language Models (LLMs) to adhere to human preferences. However, the performance of DPO…
cs.LG2024
Towards Robust Alignment of Language Models: Distributionally Robustifying Direct Preference Optimization
Junkang Wu, Yuexiang Xie, Zhengyi Yang +6
This study addresses the challenge of noise in training datasets for Direct Preference Optimization (DPO), a method for aligning Large Language Models (LLMs) with human preferences…
cs.CL2024★ 2 cited
When to Trust LLMs: Aligning Confidence with Response Quality
Shuchang Tao, Liuyi Yao, Hanxing Ding +6
Despite the success of large language models (LLMs) in natural language generation, much evidence shows that LLMs may produce incorrect or nonsensical text. This limitation highlig…