HaluNet: Detecting Hallucinations in LLM Question Answering
Published:Dec 31, 2025 02:03
•1 min read
•ArXiv
Analysis
This paper addresses the critical problem of hallucination in Large Language Models (LLMs) used for question answering. The proposed HaluNet framework offers a novel approach by integrating multiple granularities of uncertainty, specifically token-level probabilities and semantic representations, to improve hallucination detection. The focus on efficiency and real-time applicability is particularly important for practical LLM applications. The paper's contribution lies in its multi-branch architecture that fuses model knowledge with output uncertainty, leading to improved detection performance and computational efficiency. The experiments on multiple datasets validate the effectiveness of the proposed method.
Key Takeaways
- •Proposes HaluNet, a novel framework for hallucination detection in LLM question answering.
- •Integrates multi-granular token-level uncertainties (probabilistic confidence and semantic embeddings).
- •Achieves strong detection performance and computational efficiency.
- •Suitable for real-time hallucination detection in LLM-based QA systems.
Reference
“HaluNet delivers strong detection performance and favorable computational efficiency, with or without access to context, highlighting its potential for real time hallucination detection in LLM based QA systems.”