赋予AI人格可催生更类人的推理
Giving AI Personalities Leads to More Human-Like Reasoning
AI总结：
针对“完整推理谱系问题”，本研究采用基于大五人格的提示工程结合遗传算法优化，验证LLM可模拟人类推理响应分布，开源模型表现优于专有GPT模型，为提升AI推理类人性提供新路径。
摘要：
在计算认知建模领域，捕捉人类判断与决策过程的完整谱系（而非仅局限于最优行为）是一项重大挑战。本研究探讨大语言模型（LLM）能否通过预测直觉快速的系统1与审慎缓慢的系统2过程，模拟人类推理的广度，我们将这一议题称为“完整推理谱系问题”。我们采用经新型泛化处理的自然语言推理（NLI）格式设计推理任务，以评估LLM复现人类推理的能力，所设问题可同时诱发系统1与系统2响应。研究通过众包收集人类回答，且对响应的整体分布（而非仅多数答案）进行建模。我们受大五人格模型启发，采用基于人格的提示工程，诱发反映特定人格特质的AI响应，以此捕捉人类推理的多样性，并探究人格特质如何影响LLM输出。该方法结合遗传算法优化这些提示的权重，与传统机器学习模型一同接受测试。结果表明，LLM能够模拟人类响应分布，其中Llama、Mistral等开源模型表现优于专有GPT模型。基于人格的提示工程（尤其是经遗传算法优化后）显著提升了LLM预测人类响应分布的能力，这说明捕捉次优的自然主义推理，可能需要纳入多元推理风格与心理画像的建模技术。研究结论指出，基于人格的提示工程结合遗传算法，在提升AI推理的“类人性”方面颇具前景。
英文摘要：
In computational cognitive modeling, capturing the full spectrum of human judgment and decision-making processes, beyond just optimal behaviors, is a significant challenge. This study explores whether Large Language Models (LLMs) can emulate the breadth of human reasoning by predicting both intuitive, fast System 1 and deliberate, slow System 2 processes. We investigate the potential of AI to mimic diverse reasoning behaviors across a human population, addressing what we call the "full reasoning spectrum problem". We designed reasoning tasks using a novel generalization of the Natural Language Inference (NLI) format to evaluate LLMs' ability to replicate human reasoning. The questions were crafted to elicit both System 1 and System 2 responses. Human responses were collected through crowd-sourcing and the entire distribution was modeled, rather than just the majority of the answers. We used personality-based prompting inspired by the Big Five personality model to elicit AI responses reflecting specific personality traits, capturing the diversity of human reasoning, and exploring how personality traits influence LLM outputs. Combined with genetic algorithms to optimize the weighting of these prompts, this method was tested alongside traditional machine learning models. The results show that LLMs can mimic human response distributions, with open-source models like Llama and Mistral outperforming proprietary GPT models. Personality-based prompting, especially when optimized with genetic algorithms, significantly enhanced LLMs' ability to predict human response distributions, suggesting that capturing suboptimal, naturalistic reasoning may require modeling techniques incorporating diverse reasoning styles and psychological profiles. The study concludes that personality-based prompting combined with genetic algorithms is promising for enhancing AI's 'human-ness' in reasoning.
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。
