A lightweight max-pooling network with MLP detects LLM hallucinations competitively without semantic consistency computations by adaptively aggregating internal token features.
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2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2representative citing papers
ChatGPT can produce synthetic system requirement specifications that 62 percent of experts rate as realistic, though the outputs contain contradictions and deficiencies.
citing papers explorer
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Max-pooling Network Revisited: Analyzing the Role of Semantic Probability in Multiple Instance Learning for Hallucination Detection
A lightweight max-pooling network with MLP detects LLM hallucinations competitively without semantic consistency computations by adaptively aggregating internal token features.
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Can ChatGPT Generate Realistic Synthetic System Requirement Specifications? Results of a Case Study
ChatGPT can produce synthetic system requirement specifications that 62 percent of experts rate as realistic, though the outputs contain contradictions and deficiencies.