APCD: Adaptive Path-Contrastive Decoding for Reliable Large Language Model Generation
Reliable text generation is critical for deploying large language models (LLMs) in real-world applications, particularly in high-stakes domains such as medicine. To improve factual reliability, various inference-time methods have been proposed, including logit-level methods that modify token probability distributions and representation-level methods that manipulate intermediate model representations. However, most existing approaches operate on a single decoding trajectory, limiting their ability to explore alternative reasoning paths and making them susceptible to error accumulation. To address this limitation, we propose Adaptive Path-Contrastive Decoding (APCD), an adaptive multi-path ...
| Authors | Tianyu Zheng, Hong Wu, Jiaji Zhong |
|---|---|
| Primary category | cs.CL |
| Published | 10 May 2026 |
| Last updated at source | 7 October 2026 |
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