AI reliability
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Confident but Wrong: A Lifecycle Analysis of Hallucination in Large Language Models Understanding AI's Confidence Problem
Abstract: A Large Language Model (LLM) is likely to produce sentences that are fluent and confident in many instances, but completely wrong. In many cases a Large Language Model (LLM) will produce a fluent and confident sentence that is completely incorrect. This review tries to analyze this phenomenon using the most recent literature regarding natural language generation, computational learning theory and benchmark experiments. The argument is that it is not a …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 13, Issue 3, 2025 Read article
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Semantic Similarity Framework for Automatic Hallucination Detection in Large Language Models
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