通过匹配模型内在偏好，让提示词更符合大模型的思维习惯。
Inductive Bias Extraction and Matching for LLM Prompts
- 用模型输出反哺提示词生成，自动对齐模型内在偏好。
- 分类任务评分提升19%，排序任务评分提升27%。
- 适合想提升提示词效果的研究者与工程实践者。
提示工程的活跃研究表明，大语言模型对提示词措辞变化极为敏感。部分原因可归因于模型自身存在的归纳偏置。通过将大模型的输出作为提示词的一部分，能更高效地生成理想的提示表述，从而实现提示词与模型归纳偏置的匹配。实验表明，该归纳偏置提取与匹配策略可使用于分类的LLM Likert评分提升最高达19%，用于排序的评分提升最高达27%。
原文摘要 · Abstract (English)
The active research topic of prompt engineering makes it evident that LLMs are sensitive to small changes in prompt wording. A portion of this can be ascribed to the inductive bias that is present in the LLM. By using an LLM's output as a portion of its prompt, we can more easily create satisfactory wording for prompts. This has the effect of creating a prompt that matches the inductive bias in model. Empirically, we show that using this Inductive Bias Extraction and Matching strategy improves LLM Likert ratings used for classification by up to 19% and LLM Likert ratings used for ranking by up to 27%.
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。