The Paper proposes a novel solution to LLM hallucinations using a multi-agent system. This system employs specialized agents to review and refine LLM outputs, significantly reducing factual inaccuracies and improving clarity.
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Paper - https://arxiv.org/abs/2501.13946
Solution in this Paper 💡:
→ This paper proposes a multi-agent framework to mitigate LLM hallucinations.
→ It uses specialized agents in a pipeline.
→ A front-end agent generates initial responses, potentially with hallucinations.
→ Second and third-level agents review and refine these responses.
→ They use different LLMs and strategies to detect and correct unverified claims.
→ OVON framework facilitates communication between agents using JSON messages.
→ These messages carry contextual information and hallucination assessments.
→ This structured communication helps agents refine text without losing context.
→ Key Performance Indicators (KPIs) are introduced to quantify hallucination levels.
→ A fourth-level agent evaluates these KPIs to measure improvement at each stage.
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Key Insights from this Paper 🤔:
→ Multi-agent orchestration is effective in reducing LLM hallucinations.
→ Structured exchange of meta-information via NLP-based APIs is crucial.
→ OVON framework enables seamless communication and context transfer between agents.
→ Novel KPIs provide a quantifiable way to assess hallucination mitigation.
→ Iterative refinement by specialized agents significantly improves AI response reliability.
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Results 🏆:
→ Total Hallucination Scores (THS) decrease at each agent level.
→ THS mean improved from -0.0049 (Agent 1) to -0.1396 (Agent 3).
→ Hallucination score reduction of over 800% from Agent 1 to Agent 2.
→ Hallucination score reduction of nearly 2800% from Agent 1 to Agent 3.
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