量化文本视角并控制大模型输出立场，提升AI表达的可解释性。
Perspective Dial: Measuring Perspective of Text and Guiding LLM Outputs
- 构建视角空间，用数学方式衡量文本立场
- 通过反馈优化提示词，精准调整大模型输出立场
- 适合关注AI偏见、舆论分析与立场引导的研究者
大型语言模型在众多关键任务中广泛应用。由于其快速发展，目前缺乏对模型输出中偏见和视角的量化理解。为此，本文提出 Perspective-Dial，包含两个核心部分：一是名为「视角空间」的度量空间，可对特定话题的不同视角进行定量评估；二是基于贪婪坐标下降的系统化提示工程，利用视角空间的反馈来控制大模型输出的立场。该方法无需依赖对偏见或视角的先验理论理解，即可实现对多种话题下模型输出的量化测量与调节。潜在应用包括检测与缓解大模型偏见、叙事识别、公共话语中的意义建构与跟踪，以及针对特定立场的辩论机器人。
原文摘要 · Abstract (English)
Large language models (LLMs) are used in a variety of mission-critical roles. Due to the rapidly developing nature of LLMs, there is a lack of quantifiable understanding of the bias and perspective associated with LLM output. Inspired by this need, this paper considers the broader issue of perspective or viewpoint of general text and perspective control of large-language model (LLM) output. Perspective-Dial consists of two main components: a (1) metric space, dubbed Perspective Space, that enables quantitative measurements of different perspectives regarding a topic, and the use of (2) Systematic Prompt Engineering that utilizes greedy-coordinate descent to control LLM output perspective based on measurement feedback from the Perspective Space. The empirical nature of the approach allows progress to side step a principled understanding of perspective or bias -- effectively quantifying and adjusting outputs for a variety of topics. Potential applications include detection, tracking and mitigation of LLM bias, narrative detection, sense making and tracking in public discourse, and debate bot advocating given perspective.
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