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cs.CL2025
Resisting Contextual Interference in RAG via Parametric-Knowledge Reinforcement
Chenyu Lin, Yilin Wen, Du Su +5
Retrieval-augmented generation (RAG) improves performance on knowledge-intensive tasks but can be derailed by wrong, irrelevant, or conflicting retrieved text, causing models to re…
cs.CL2025
Too Consistent to Detect: A Study of Self-Consistent Errors in LLMs
Hexiang Tan, Fei Sun, Sha Liu +8
As large language models (LLMs) often generate plausible but incorrect content, error detection has become increasingly critical to ensure truthfulness. However, existing detection…