A lightweight max-pooling network with MLP detects LLM hallucinations competitively without semantic consistency computations by adaptively aggregating internal token features.
A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions
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StructSense is a task-agnostic agentic framework for structured information extraction that reports 91-100% accuracy on schema-based tasks, 86-93% on metadata extraction, and 58-75% NER accuracy while adding entities beyond gold standards on biomedical benchmarks.
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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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STRUCTSENSE: A Task-Agnostic Agentic Framework for Structured Information Extraction with Human-In-The-Loop Evaluation and Benchmarking
StructSense is a task-agnostic agentic framework for structured information extraction that reports 91-100% accuracy on schema-based tasks, 86-93% on metadata extraction, and 58-75% NER accuracy while adding entities beyond gold standards on biomedical benchmarks.