# LLM Agents in Interaction: Measuring Personality Consistency and Linguistic Alignment in Interacting Populations of Large Language Models

**Authors:** Ivar Frisch, Mario Giulianelli  
**arXiv:** [2402.02896](https://arxiv.org/abs/2402.02896)  
**Date:** 2024-02-05  
**Summary page:** https://www.summarizepaper.com/en/paper/2402.02896v1/

## Key points

- Researchers study interaction between large language model (LLM) agents, focusing on personality consistency and linguistic alignment.
- Persona-conditioned LLM agents aim to maintain assigned traits in dialogues through conditioning and variability-inducing sampling.
- Future plans include enhancing experimental setup with diverse personality profiles, multi-turn interactions, and measuring alignment at lexical, syntactic, and semantic levels.
- Need for new methods to ensure personality consistency and control linguistic adaptation in LLM personas is highlighted.
- Exploration of sequential pattern mining for lexical alignment detection and word embeddings for estimating lexical semantic variation across personas is suggested.
- Proposal to use more robust generators like GPT-4 due to concerns about story quality generated by GPT-3.5.
- Importance of exploring different prompting strategies' impact on personality consistency and lexical alignment is emphasized.
- Ethical implications of AI agents interacting with humans are discussed, advocating for transparency in AI usage for ethical engagement with technology.

## Summary

In this study, the researchers delve into the interaction between large language model (LLM) agents and the impact of personality consistency and linguistic alignment on their behavior. They focus on persona-conditioned LLM agents and aim to ensure that these agents maintain their assigned traits while engaging in naturalistic dialogues. By conditioning GPT-3.5 on various personality profiles through prompting, they create a diverse population of LLM agents using a variability-inducing sampling algorithm. Moving forward, the researchers plan to enhance their experimental setup by introducing more diverse and detailed personality profiles, enabling multi-turn interactions between agents, and measuring alignment at different levels such as lexical, syntactic, and semantic in accordance with the Interactive Alignment framework. They also highlight the need for new methods to guarantee personality consistency and control linguistic adaptation in LLM personas. Acknowledging the limitations of their exploratory work, the researchers emphasize the importance of investigating multi-turn dialogic interactions and utilizing advanced measures for personality and linguistic alignment. They suggest exploring sequential pattern mining approaches for lexical alignment detection and leveraging word embeddings for estimating lexical semantic variation across personas. Furthermore, they address concerns regarding story quality generated by GPT-3.5 and propose using more robust generators like GPT-4 for future studies. The researchers also stress the significance of exploring different prompting strategies to assess their impact on personality consistency and lexical alignment. Ethically, they underscore potential implications of AI agents interacting with humans, particularly in reproducing human traits artificially. They advocate for transparency in AI usage to build trust and ensure ethical engagement with technology. Overall, this study sets a foundation for understanding dialogue-based interactions between LLMs while emphasizing the importance of crafting robust and human-like personas for interactive environments.