为什么总体准确率会在交互式 LLM 评测中产生误导：应验证比较结果而非只验证打分器
Validating the Comparison, Not Just the Scorer: Why Overall Accuracy Can Mislead in Interactive LLM Evaluation
AI 导读
一项 arXiv 研究指出，仅用总体准确率或 F1 验证自动打分器，可能掩盖特定提示词下的错误，使对话压力测量结果被夸大、缩小甚至方向反转。在 6 个模型、180 个虚构实体问题、6 种提示条件下，288 条回复的总体 F1 为 .916，但打分器误报率随提示在 2.6%-33.3% 间波动；表面 9.3 点的确定性断言效应在双评分者复核下降至 2.3 点，区间包含零。
正文 · 原文
Abstract:Many LLM studies compare how models respond to different follow-up prompts. For example, one prompt may neutrally ask a model to reconsider, while another insists that an invented film really exists. The difference is used to measure the effect of conversational pressure. An automatic scorer then labels whether each response makes an unsupported factual claim. Researchers commonly validate the scorer with one overall accuracy or $F_1$ score. However, high overall performance can hide prompt-specific mistakes: if the scorer makes more errors after one prompt, the measured difference can be too large, too small, or point in the wrong direction. An audit containing mostly difficult responses can also misrepresent performance in the full study. We derive how such errors alter a prompt comparison and examine the problem using six models, 180 invented-entity questions, and six prompt conditions. In a balanced audit of 288 responses, overall $F_1$ was $.916$. Yet, depending on the prompt, the scorer flagged 2.6%-33.3% of responses that the primary LLM rater judged not to contain an unsupported claim; these prompt-specific estimates were uncertain. In a second audit, the scorer and two separate LLM raters evaluated the same 300 model-question cases under each prompt. An apparent 9.3-point increase from asserting certainty rather than neutrally asking the model to reconsider fell to 2.3 points under both raters. The primary-rater interval included zero, so it did not establish an increase. Two other prompt comparisons kept their direction under both raters, while an attempted prompt-specific correction remained too uncertain for a firm conclusion. Researchers should therefore validate the exact difference they plan to report, not just the scorer as a whole: audit both prompts on matching cases and include scorer and rater uncertainty in the final result.
| Comments: | 14 pages, 2 figures. Accepted to the Trustworthy AI for Good (AI4GOOD) Workshop at NeurIPS 2026 |
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC) |
| Cite as: | arXiv:2610.31678 [cs.CL] |
| (or arXiv:2610.31678v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2610.31678 arXiv-issued DOI via DataCite |
Submission history
From: Ancil Crayton [view email]
[v1]
Thu, 1 Oct 2026 03:02:54 UTC (85 KB)
来源：arXiv cs.AI · arxiv.org