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First Hallucination Tokens Are Different from Conditional Ones
Created by
Haebom
Author
Jakob Snel, Seong Joon Oh
Outline
This paper studies token-level detection of hallucinations (creating false content), a key issue with baseline models. By leveraging the RAGTruth corpus and analyzing token-level annotations and reconstructed logits, we analyze how hallucination signals vary depending on the token's position within the hallucinated segment. The analysis results reveal that the first hallucination token has a stronger signal than the conditional token and is easier to detect. Furthermore, we publicly released an analysis framework, along with code for logit reconstructing and metric calculations ( https://github.com/jakobsnl/RAGTruth_Xtended ).
By identifying the difference in detectability according to the location of the hallucination token, it can contribute to improving the performance of hallucination detection per token.
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The efficiency of hallucination detection can be improved by focusing on the first hallucination token.
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The published analysis framework and code can be utilized for future research.
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Limitations:
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High dependence on the RAGTruth corpus. Generalizability to other corpora is required.
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Because the analysis target is limited to a specific corpus and model, additional generalization research for various environments is needed.
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Due to the limitations of token-level analysis, the difficulty of detecting hallucinations based on contextual understanding remains.
