12 citations · 12 across the 7 of their papers we have counts for
4 papers · 2 filters
PMoL: Parameter Efficient MoE for Preference Mixing of LLM Alignment
Dongxu Liu, Bing Xu, Yinzhuo Chen +4
Reinforcement Learning from Human Feedback (RLHF) has been proven to be an effective method for preference alignment of large language models (LLMs) and is widely used in the post-…
Mitigating the Bias of Large Language Model Evaluation
Hongli Zhou, Hui Huang, Yunfei Long +5
Recently, there has been a trend of evaluating the Large Language Model (LLM) quality in the flavor of LLM-as-a-Judge, namely leveraging another LLM to evaluate the current output…
Self-Evaluation of Large Language Model based on Glass-box Features
Hui Huang, Yingqi Qu, Jing Liu +4
The proliferation of open-source Large Language Models (LLMs) underscores the pressing need for evaluation methods. Existing works primarily rely on external evaluators, focusing o…
An Empirical Study of LLM-as-a-Judge for LLM Evaluation: Fine-tuned Judge Model is not a General Substitute for GPT-4
Hui Huang, Xingyuan Bu, Hongli Zhou +5
Recently, there has been a growing trend of utilizing Large Language Model (LLM) to evaluate the quality of other LLMs. Many studies have fine-tuned judge models based on open-sour…