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SELD: Detecting Silent Evidence Loss in Context Compression beyond the Answer
Not provided in the abstract
context compressionevidence detectionlanguage modelsquestion answering
2611.12903
Builder Relevance
8h ago80%
Abstract
The paper introduces SELD, a method for detecting silent evidence loss in compressed contexts of language models, which can occur even when the answer remains unchanged.
Reality Card
Core Claim
SELD consistently outperforms existing baselines in identifying evidence loss in compressed contexts, achieving an 83% detection rate of evidence-removing edits with unchanged answers.
Method / Result
Identified evidence-removing edits in 83% of matched pairs with unchanged answers, compared to 70% for the strongest baseline.
Limitations
The paper does not specify the authors or provide detailed reproducibility concerns.
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