Context-Fidelity Boosting: Enhancing Faithful Generation through Watermark-Inspired Decoding
Year: 2026
Type: conference-paper
Abstract: 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-supported tokens.Motivated by logit-shaping principles from watermarking techniques, CFB applies additive tokenlevel logit adjustments based on whether a token is supported by the input context and how strongly it is supported.Specifically, we develop three boosting strategies: static boosting, which applies a fixed bias to source-supported tokens; context-aware boosting, which scales this bias using the divergence between contextaware and context-free next-token distributions; and token-aware boosting, which further redistributes the adaptive bias according to local relevance estimated from source-position attention and source-scoped semantic similarity.CFB requires no retraining or architectural changes, making it compatible with a wide range of LLMs.Experiments on summarization and question answering tasks across multiple opensource LLMs show that CFB consistently improves faithfulness metrics with minimal generation overhead.Our implementation is fully open-sourced.1
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7166828044