插值解码：探索大语言模型中人格特质的光谱
Interpolative Decoding: Exploring the Spectrum of Personality Traits in LLMs
AI总结：
通过插值解码技术，研究者探索了大语言模型中人格特质的光谱，展示了如何通过插值参数模拟不同人格维度的行为，从而在经济游戏中复现人类决策行为。
摘要：
最近的研究探讨了使用非常大的语言模型（LLMs）作为人类在模拟、调查和研究等任务中的代理。虽然LLMs没有人类心理学，但它们经常能够以足够高的保真度模拟人类行为，从而驱动模拟来测试人类行为假设，表现出比行为经济学中通常使用的规则代理更细致和更广泛的范围。一个关键的研究领域是人格对决策的影响，但为每个测试的人格配置文件创建提示的要求引入了实验开销并降低了可重复性。为了解决这个问题，我们利用插值解码，将每个人格维度表示为一对对立的提示，并利用插值参数来模拟该维度的行为。我们证明插值解码能够可靠地调节每个大五维度的分数。然后我们展示了插值解码如何使LLMs在经济游戏中模仿人类决策行为，复制了人类心理研究的结果。最后，我们展示了通过系统搜索插值空间中的点来“双人”个别人类玩家在合作游戏中的初步结果。
英文摘要：
Recent research has explored using very large language models (LLMs) as proxies for humans in tasks such as simulation, surveys, and studies. While LLMs do not possess a human psychology, they often can emulate human behaviors with sufficiently high fidelity to drive simulations to test human behavioral hypotheses, exhibiting more nuance and range than the rule-based agents often employed in behavioral economics. One key area of interest is the effect of personality on decision making, but the requirement that a prompt must be created for every tested personality profile introduces experimental overhead and degrades replicability. To address this issue, we leverage interpolative decoding, representing each dimension of personality as a pair of opposed prompts and employing an interpolation parameter to simulate behavior along the dimension. We show that interpolative decoding reliably modulates scores along each of the Big Five dimensions. We then show how interpolative decoding causes LLMs to mimic human decision-making behavior in economic games, replicating results from human psychological research. Finally, we present preliminary results of our efforts to ``twin'' individual human players in a collaborative game through systematic search for points in interpolation space that cause the system to replicate actions taken by the human subject.
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