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October 13, 20250 citationsOpen Access

HalluDetect: Mitigating Hallucinations in Conversational AI Systems

HalluDetect: Detecting, Mitigating, and Benchmarking Hallucinations in Conversational Systems

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Authors

SASpandan AnaokarSGShrey GanatraHKHarshvivek Kashid

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Overview

HalluDetect shows a 25.44% improvement in hallucination detection for LLM-based chatbots, suggesting enhanced trust in consumer grievance applications.

Key Points

  • HalluDetect achieves an F1 score of 69%, successfully outpacing baseline methods.
  • AgentBot, one of the architectures tested, minimizes hallucinations to 0.4159 per turn, with a token accuracy of 96.13%.
  • The study benchmarks five chatbot architectures, demonstrating effectiveness in mitigating hallucinations.
  • Findings highlight the potential for enhanced factual accuracy in LLM-driven assistants, applicable across various high-risk sectors.

Cite This Study

Anaokar et al. (2025) studied this question.

synapsesocial.com/papers/68ecfebf950606aabec0952dhttps://doi.org/10.48550/arxiv.2509.11619
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