hallucination detection

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• Published Subscription Review Article 4 Jul 2026

  Semantic Similarity Framework for Automatic Hallucination Detection in Large Language Models

  By Sujal Gupta, Hardik Sharma, Shivansh Narayan, Dhruv Kumar, Aayush Bisht

  Abstract: Large Language Models can generate fluent, contextually appropriate text across a range of NLP tasks, but they frequently produce outputs that are factually wrong while sounding confident and plausible. This problem, referred to as hallucination, poses serious risks in domains where accuracy matters. We propose a post-processing framework that detects hallucinated responses by comparing them against verified reference text using sentence embeddings. The system computes cosine similarity between the response …

  Published in Emerging Trends in Languages · Vol. 3, Issue 2, 2026 · pp. 16–22 Read article →