CAAD: Context-Aware Adaptive Decoding for Truthful Text Generation

• URL: http://arxiv.org/abs/2508.02184v1
• Date: Mon, 04 Aug 2025 08:28:25 GMT
• Title: CAAD: Context-Aware Adaptive Decoding for Truthful Text Generation
• Authors: Manh Nguyen, Sunil Gupta, Hung Le,
• Abstract summary: We propose a context-aware adaptive decoding method for large language models.<n>Our approach achieves a 2.8 percent average improvement on TruthfulQA.<n>Our model-agnostic, scalable, and efficient method requires only a single generation pass.
• Score: 31.469511576774252
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Ensuring truthfulness in large language models remains a critical challenge for reliable text generation. While supervised fine-tuning and reinforcement learning with human feedback have shown promise, they require substantial amount of annotated data and computational resources, limiting scalability. In contrast, decoding-time interventions offer lightweight alternatives without model retraining. However, existing decoding strategies often face issues like prompt sensitivity, limited generalization, or dependence on internal model states. We propose a context-aware adaptive decoding method that leverages a compact reference grounding space, built from as few as 10 annotated examples and comprising pairs of context embeddings and next token logits from truthful responses, to enable retrieval-based logit shaping during inference. At each decoding step, our method retrieves top-N semantically similar contexts and aggregates their associated next token logits to modify the LLM's logits. Across three open-ended question-answering benchmarks, our approach achieves a 2.8 percent average improvement on TruthfulQA and further outperforms existing baselines on both Biographies and WikiQA. Experimental results also demonstrate cross-task generalization, with TruthfulQA-derived grounding enhancing biography generation. Our model-agnostic, scalable, and efficient method requires only a single generation pass, highlighting the potential of context-aware decoding for factual reliability in LLMs.

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