SFAD: Speculative Factuality-Aware Decoding
As one of the most critical challenges in large language models, contextual faithfulness directly determines their reliability in knowledge-intensive applications. This task is particularly challenging as it requires balancing factual consistency with generation efficiency. Contrastive decoding methods require dual forward passes (with and without context) to compare model outputs, doubling inference computational overhead, while post-training alignment demands extensive reinforcement learning with substantial computational overhead. To address this challenge, we present SFAD, a speculative decoding framework that enhances contextual faithfulness without inference degradation. We first co...
| Authors | Guanqiao Chen, Di Wang, Lijie Hu |
|---|---|
| Primary category | cs.CL |
| Published | 1 September 2026 |
| Last updated at source | 3 September 2026 |
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