评估评估者：考察 LLM 评审在个性化写作风格评估中的表现

AuthorsNatalie Mackraz‡, Andrew Silva, Aini Putkonen, Brihi Joshi†**, Joelle Alcaidinho, Arnav Arora§**, Barry-John Theobald, Katherine Metcalf‡

This paper was accepted at the Can We Trust AI Evaluation? Workshop at NeurIPS 2026.

We evaluate how good large language model (LLM) judges are for personalized writing tasks. This is important when a user’s preferred writing style is known (e.g., “upbeat”) and an LLM judge is used to evaluate whether generated text adheres to this preference. We examine how the writing task, the combination of generator and judge LLM, the evaluation objective, and general commonsense and reasoning ability impact LLM judge performance. To this end, we generate text with four LLMs for three long-form writing tasks, collect human labels evaluating which of two responses is better, collect judge-LLM, pairwise-ranking annotations, and then compare the human and judge LLM annotations. We find that judge quality correlates weakly with general LLM ability measured using MMLU (Pearson’s ρ ≤ 0.2) and varies by both writing task and evaluation criteria (i.e., general helpfulness versus style personalization). We also find that LLM evaluators are more consistent and performant using paired comparisons rather than scoring responses independently. Finally, we find that the “strongest” LLMs do not most reliably reproduce the human evaluations, which we hypothesize relates to exaggerated sensitivity to subtle details that human annotators and weaker LLMs ignore.

• ‡ Equal contribution
• † University of Southern California
• § University of Copenhagen
• ** Work done while at Apple

Related readings and updates.

Large language models (LLMs) exhibit a wide range of human-like behaviors, from expressing thoughts and emotions, to engaging in relationship-building with users, to refusing requests and maintaining boundaries. Despite their prevalence, researchers and practitioners lack methods and empirical insights to make informed decisions about when and what types of human-like behaviors LLMs should exhibit. To fill this gap, we present a multi-dimensional…

Read more

Accommodating human preferences is essential for creating aligned LLM agents that deliver personalized and effective interactions. Recent work has shown the potential for LLMs acting as writing agents to infer a description of user preferences. Agent alignment then comes from conditioning on the inferred preference description. However, existing methods often produce generic preference descriptions that fail to capture the unique and…

Read more

注意转载授权 · 阅读原文