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cs.CL2025
Users as Annotators: LLM Preference Learning from Comparison Mode
Zhongze Cai, Xiaocheng Li
Pairwise preference data have played an important role in the alignment of large language models (LLMs). Each sample of such data consists of a prompt, two different responses to t…
cs.GT2025
Incentivizing High-Quality Human Annotations with Golden Questions
Shang Liu, Zhongze Cai, Hanzhao Wang +2
Human-annotated data plays a vital role in training large language models (LLMs), such as supervised fine-tuning and human preference alignment. However, it is not guaranteed that…
cs.LG2025
How Humans Help LLMs: Assessing and Incentivizing Human Preference Annotators
Shang Liu, Hanzhao Wang, Zhongyao Ma +1
Human-annotated preference data play an important role in aligning large language models (LLMs). In this paper, we study two connected questions: how to monitor the quality of huma…