AI RESEARCH
Context-Fidelity Boosting: Enhancing Faithful Generation through Watermark-Inspired Decoding
arXiv CS.CL
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ArXi:2604.22335v1 Announce Type: new Large language models (LLMs) often produce content that contradicts or overlooks information provided in the input context, a phenomenon known as faithfulness hallucination. In this paper, we propose Context-Fidelity Boosting (CFB), a lightweight and general decoding-time framework that reduces such hallucinations by increasing the generation probability of source-ed tokens. Motivated by logit-shaping principles from watermarking techniques, CFB applies additive token-level logit adjustments based on a token's degree of from the input context.