自我批判引导的好奇心细化：通过上下文学习增强大型语言模型的诚实性与有用性
Self-Critique-Guided Curiosity Refinement: Enhancing Honesty and Helpfulness in Large Language Models via In-Context Learning
AI总结：
本文提出自我批判引导的好奇心细化提示策略，通过两个轻量级上下文学习步骤提升LLM的诚实性和有用性，在HONESET数据集上实现H²分数1.4%-4.3%的相对提升。
摘要：
大型语言模型（LLMs）在各种自然语言任务中展现了强大的能力。然而，生成始终诚实且有用的输出仍然是一个开放的挑战。为了克服这一挑战，本文通过两个互补的方向来解决该问题。首先，它对十个广泛使用的大型语言模型进行了全面的基准评估，包括来自OpenAI、Meta和Google的专有模型和开放权重模型。同时，本文提出了一种新颖的提示策略，即自我批判引导的好奇心细化提示。该策略的关键思想是使模型能够在不进行额外训练的情况下自我批判并细化其响应。所提出的方法通过引入两个轻量级的上下文学习步骤（包括自我批判步骤和细化步骤）扩展了好奇心驱动的提示策略。\n在HONESET数据集上使用$\nmathrm{H}^2$（诚实性和有用性）框架进行的实验结果显示，所有模型均获得了一致的改进，该框架使用GPT-4o作为诚实性和有用性的评判者。该方法减少了低质量响应的数量，增加了高质量响应，并且与好奇心驱动的提示相比，在评估模型上的$\nmathrm{H}^2$分数取得了1.4%至4.3%的相对提升。这些结果突显了结构化自我细化作为一种可扩展且无需训练的策略，在提高LLMs输出的可信度方面的有效性。
英文摘要：
Large language models (LLMs) have demonstrated robust capabilities across various natural language tasks. However, producing outputs that are consistently honest and helpful remains an open challenge. To overcome this challenge, this paper tackles the problem through two complementary directions. It conducts a comprehensive benchmark evaluation of ten widely used large language models, including both proprietary and open-weight models from OpenAI, Meta, and Google. In parallel, it proposes a novel prompting strategy, self-critique-guided curiosity refinement prompting. The key idea behind this strategy is enabling models to self-critique and refine their responses without additional training. The proposed method extends the curiosity-driven prompting strategy by incorporating two lightweight in-context steps including self-critique step and refinement step. The experiment results on the HONESET dataset evaluated using the framework $\mathrm{H}^2$ (honesty and helpfulness), which was executed with GPT-4o as a judge of honesty and helpfulness, show consistent improvements across all models. The approach reduces the number of poor-quality responses, increases high-quality responses, and achieves relative gains in $\mathrm{H}^2$ scores ranging from 1.4% to 4.3% compared to curiosity-driven prompting across evaluated models. These results highlight the effectiveness of structured self-refinement as a scalable and training-free strategy to improve the trustworthiness of LLMs outputs.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
