Mitigating LLM Hallucination Snowballing in Multiagent Systems via Context-Aware Semantic Consistency Reasoning

Area of Science:

• Artificial Intelligence
• Natural Language Processing
• Multiagent Systems

Background:

• Large language models (LLMs) enable advanced multiagent collaborations.
• LLM-generated untruthful content leads to hallucination snowballing in collaborations.
• Existing research lacks analysis of hallucinations in multiagent settings.

Purpose of the Study:

• To propose a framework for analyzing hallucination propagation in sequential multiagent collaboration.
• To develop methods for validating and mitigating the hallucination snowballing effect.
• To address hallucination propagation without altering model architecture.

Main Methods:

• Context-aware hallucination analysis framework using probabilistic modeling.
• Token-level disruption sequence detection for effect validation.
• Semantic reasoning-empowered mitigation using bidirectional entailment clustering.

Main Results:

• Validated the existence of hallucination snowballing in multiagent collaborations across domains.
• Demonstrated effective reduction of hallucination propagation using the proposed mitigation strategy.
• Showcased mitigation of hallucinations from both model-generated content and external knowledge gaps.

Conclusions:

• Multiagent LLM collaborations are susceptible to hallucination snowballing.
• The proposed framework effectively analyzes and mitigates this effect.
• Semantic reasoning offers a viable approach to enhance reliability in collaborative AI systems.