Hallucination Mitigation via Introspection and Cross-Modal Multi-Agent Collaboration
Published:Dec 2, 2025 17:59
•1 min read
•ArXiv
Analysis
This research, published on ArXiv, focuses on addressing the problem of hallucinations in large language models (LLMs). The approach involves two key strategies: introspection, which likely refers to the model's self-assessment of its outputs, and cross-modal multi-agent collaboration, suggesting the use of multiple agents with different modalities (e.g., text, image) to verify and refine the generated content. The title indicates a focus on improving the reliability and trustworthiness of LLMs.
Key Takeaways
- •Addresses the problem of hallucinations in LLMs.
- •Employs introspection for self-assessment.
- •Utilizes cross-modal multi-agent collaboration for verification.
- •Aims to improve the reliability and trustworthiness of LLMs.
Reference
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