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Detecting Hallucination in LLMs: Tracing the Topological Signatures of Impaired Context Sharing
Author1, Author2, Author3, Author4, Author5
attention mechanismshallucination detectioninformation flowtransformer models
2609.21096
Builder Relevance
1h ago80%
Abstract
This work examines the topology of information flow patterns within attention graphs to distinguish hallucinated from non-hallucinated responses.
Reality Card
Core Claim
The proposed single-pass approach consistently improves hallucination detection across multiple LLMs and benchmarks by analyzing information flow patterns in attention graphs.
Method / Result
Achieved consistent improvements over existing baselines across two hallucination-detection benchmarks.
Limitations
The method's reliance on specific attention graph characteristics may limit its applicability to all LLM architectures.
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