Active Indexing with synthetic data augmentation for bidirectional fact-source binding during pretraining yields up to 30.2% higher citation precision than passive identifier appending on CitePretrainBench for Qwen models.
Automatic evaluation of attribution by large language models
2 Pith papers cite this work. Polarity classification is still indexing.
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Binary groundedness judgments in AI evaluations should be replaced by a reader-centered taxonomy of support relations that distinguishes syntactic and interpretive moves between generated statements and source documents.
citing papers explorer
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Cite Pretrain: Retrieval-Free Knowledge Attribution for Large Language Models
Active Indexing with synthetic data augmentation for bidirectional fact-source binding during pretraining yields up to 30.2% higher citation precision than passive identifier appending on CitePretrainBench for Qwen models.
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From Binary Groundedness to Support Relations: Towards a Reader-Centred Taxonomy for Comprehension of AI Output
Binary groundedness judgments in AI evaluations should be replaced by a reader-centered taxonomy of support relations that distinguishes syntactic and interpretive moves between generated statements and source documents.