Papers by Kailin Zhang

1 papers
QuCo-RAG: Quantifying Uncertainty from the Pre-training Corpus for Dynamic Retrieval-Augmented Generation (2026.findings-acl)

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Challenge: Existing methods for reducing LLM hallucinations rely on model-internal signals . Existing approaches rely only on model internal signals, resulting in unreliability .
Approach: They propose a method that shifts from subjective confidence to objective statistics . they leverage Infini-gram for millisecond-latency queries over 4 trillion tokens .
Outcome: The proposed method reduces hallucinations in large language models by reducing uncertainty in the model.

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