Summary of Persllm: a Personified Training Approach For Large Language Models, by Zheni Zeng et al.
PersLLM: A Personified Training Approach for Large Language Models
by Zheni Zeng, Jiayi Chen, Huimin Chen, Yukun Yan, Yuxuan Chen, Zhenghao Liu, Zhiyuan Liu, Maosong Sun
First submitted to arxiv on: 17 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Large language models (LLMs) have made significant progress in exhibiting human-like intelligence, but their lack of distinct personalities hinders their adoption in real-world applications. To address this limitation, researchers have been exploring ways to personify LLMs, allowing them to display ingratiating behaviors, inconsistent opinions, and uniform response patterns. However, existing methods only capture superficial linguistic styles rather than the core of personalities. This study proposes PersLLM, a comprehensive training methodology that integrates psychology-grounded principles of personality into LLMs. By incorporating personality traits directly into model parameters, PersLLM enhances resistance to induction, promotes consistency, and supports dynamic evolution of personality. Experimental results demonstrate the superiority of PersLLM in producing responses aligned with reference personalities, enhancing opinion consistency within individual agents, and fostering collaborative creativity among multiple agents. The practical implications of this research highlight its potential benefits in human simulation, multi-agent cooperation, and interactive experiences. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine having a computer program that can think and behave like a person. This is what large language models (LLMs) are capable of doing. However, these models don’t have personalities, which makes them less useful for certain tasks. Researchers want to change this by making LLMs more human-like. They propose a new way to train these models called PersLLM. This method helps the models develop their own personalities and behave consistently. The results show that PersLLM is better than existing methods in many ways, including producing responses that match human personalities. This could have important implications for things like simulating human behavior and working together with computers. |
