一个分隔符字符竟能让大模型评测结果波动23%。
A Single Character can Make or Break Your LLM Evals
- 用不同符号分隔示例，影响模型输出质量
- 同一模型在不同分隔符下表现相差高达23%
- 适合关注评测公平性与提示工程的读者
主流大语言模型评估依赖示例引导模型生成期望风格的回答。尽管示例数量已标准化，但示例间的格式（如逗号、换行、分号、井号等）选择却未受重视。我们发现，这一看似微小的选择会显著影响模型表现：在Llama、Qwen、Gemma等主流模型上，MMLU任务性能因分隔符不同可波动±23%。甚至仅通过修改分隔符，就能任意操控模型排名。该脆弱性贯穿不同主题与模型家族，且不随模型规模增大而改善。通过分析注意力头得分，发现表现好的分隔符能有效引导注意力聚焦于输入关键词。最后我们提出，在提示中明确指定分隔符可提升鲁棒性，并推荐了最优分隔符方案。
原文摘要 · Abstract (English)
Common Large Language model (LLM) evaluations rely on demonstration examples to steer models' responses to the desired style. While the number of examples used has been studied and standardized, the choice of how to format examples is less investigated. In evaluation protocols and real world usage, users face the choice how to separate in-context examples: use a comma? new line? semi-colon? hashtag? etc.? Surprisingly, we find this seemingly minor choice can dramatically alter model response quality. Across leading model families (Llama, Qwen, Gemma), performance on MMLU for example can vary by $\pm 23\%$ depending on the choice of delimiter. In fact, one can manipulate model rankings to put any model in the lead by only modifying the single character separating examples. We find LLMs' brittleness pervades topics, model families, and doesn't improve with scale. By probing attention head scores, we find that good-performing delimiters steer attention towards key tokens in the input. Finally, we explore methods to improve LLMs' robustness to the choice of delimiter. We find specifying the selected delimiter in the prompt boosts robustness and offer practical recommendations for the best-performing delimiters to select.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。