大型語言模型中的人格特質

摘要

大型語言模型（LLMs）的出現已經徹底改變了自然語言處理，使得能夠生成連貫且上下文相關的文本成為可能。隨著LLMs越來越多地驅動對話代理，這些模型中嵌入的合成個性，由於它們在大量人類生成的數據上進行訓練，引起了人們的關注。由於個性是決定溝通效果的重要因素，我們提出了一種全面的方法，用於管理經過驗證的心理測試，並量化、分析和塑造從廣泛使用的LLMs生成的文本中展現的個性特徵。我們發現：1）在某些LLMs的輸出中（在特定提示配置下）模擬的個性是可靠和有效的；2）對於較大且經過指導微調的模型，LLM模擬的個性的可靠性和有效性證據更為明確；以及3）LLM輸出中的個性可以沿著所需的維度塑造，以模仿特定的個性配置文件。我們還討論了我們的測量和塑造框架的潛在應用和道德影響，特別是關於LLMs的負責任使用。

English

The advent of large language models (LLMs) has revolutionized natural language processing, enabling the generation of coherent and contextually relevant text. As LLMs increasingly power conversational agents, the synthesized personality embedded in these models by virtue of their training on large amounts of human-generated data draws attention. Since personality is an important factor determining the effectiveness of communication, we present a comprehensive method for administering validated psychometric tests and quantifying, analyzing, and shaping personality traits exhibited in text generated from widely-used LLMs. We find that: 1) personality simulated in the outputs of some LLMs (under specific prompting configurations) is reliable and valid; 2) evidence of reliability and validity of LLM-simulated personality is stronger for larger and instruction fine-tuned models; and 3) personality in LLM outputs can be shaped along desired dimensions to mimic specific personality profiles. We also discuss potential applications and ethical implications of our measurement and shaping framework, especially regarding responsible use of LLMs.

大型語言模型中的人格特質

Personality Traits in Large Language Models

摘要

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