2 citations · 4 across the 2 of their papers we have counts for
2 papers
cs.CL2023★ 2 cited
TencentLLMEval: A Hierarchical Evaluation of Real-World Capabilities for Human-Aligned LLMs
Shuyi Xie, Wenlin Yao, Yong Dai +11
Large language models (LLMs) have shown impressive capabilities across various natural language tasks. However, evaluating their alignment with human preferences remains a challeng…
cs.CL2023★ 2 cited
Everyone Deserves A Reward: Learning Customized Human Preferences
Pengyu Cheng, Jiawen Xie, Ke Bai +2
Reward models (RMs) are essential for aligning large language models (LLMs) with human preferences to improve interaction quality. However, the real world is pluralistic, which lea…