Summary of Exploring and Controlling Diversity in Llm-agent Conversation, by Kuanchao Chu et al.

Main: Computation and Language (cs.CL)
Secondary: Artificial Intelligence (cs.AI)

Exploring and Controlling Diversity in LLM-Agent Conversation

by KuanChao Chu, Yi-Pei Chen, Hideki Nakayama

First submitted to arxiv on: 30 Dec 2024

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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 In this research paper, the authors investigate how to maintain conversational diversity in long-term simulations of multiple intelligent agents (LLM-agents). They find that reducing the amount of given information in prompts leads to more diverse outputs. To address this issue, they propose Adaptive Prompt Pruning (APP), a novel method that allows users to control diversity through a single parameter, lambda. APP dynamically prunes the utterance generation prompt based on attention weights and is compatible with traditional diversity control techniques.
Low	GrooveSquid.com (original content)	Low Difficulty Summary In simple terms, this paper is about how to make conversations between many intelligent agents more interesting and varied over time. The authors discovered that giving them too much information makes their responses less diverse. To fix this, they created a new way to control the amount of information given to these agents, which they call Adaptive Prompt Pruning (APP). This method helps balance the trade-off between stability and creativity in conversations.