Papers with training-free decoding strategy
DeCoRe: Decoding by Contrasting Retrieval Heads to Mitigate Hallucinations (2025.findings-emnlp)
Copied to clipboard
Aryo Pradipta Gema, Chen Jin, Ahmed Abdulaal, Tom Diethe, Philip Alexander Teare, Beatrice Alex, Pasquale Minervini, Amrutha Saseendran
| Challenge: | Large Language Models often produce unfaithful or factually incorrect outputs . masking retrieval heads can induce hallucinations, but decoding by contrast can reduce hallucinosity . |
| Approach: | They propose a training-free decoding strategy that contrasts the outputs of the base LLM and the masked LLM. |
| Outcome: | The proposed decoding strategy reduces hallucinations by contrasting the outputs of the base and masked LLMs. |
DOS: Dependency-Oriented Sampler for Masked Diffusion Language Models (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing decoding strategies for pre-trained MDLMs rely on token-level uncertainty criteria, while largely overlooking sequence-level information and inter-token dependencies. |
| Approach: | They propose a training-free decoding strategy that leverages inter-token dependencies to inform token updates during generation. |
| Outcome: | Empirical results show that the proposed approach consistently achieves superior performance on both code generation and mathematical reasoning tasks. |
Fixing Semantic Blind Spots in Anchor Tokens of dMLLMs (2026.findings-acl)
Copied to clipboard
| Challenge: | Autoregressive models (ARMs) are prone to hallucinations due to their sequential text generation and high latency. |
| Approach: | They propose a training-free decoding strategy that augments the attention key space with a static, distance-aware matrix to reduce the attention sink effect on semantic anchors. |
| Outcome: | The proposed method reduces the attention sink effect on semantic anchors while enhancing their ability to aggregate global visual information. |
Cautious Next Token Prediction (2025.findings-acl)
Copied to clipboard
Yizhou Wang, Lingzhi Zhang, Yue Bai, Mang Tik Chiu, Zhengmian Hu, Mingyuan Zhang, Qihua Dong, Yu Yin, Sohrab Amirghodsi, Yun Fu
| Challenge: | Existing methods for decoding autoregressive models are temperature scaling and nucleus sampling to balance diversity and coherence. |
| Approach: | They propose a training-free decoding strategy that uses a model with a low perplexity score to select the trial with the lowest perplexities as the most probable and reliable path. |
| Outcome: | The proposed approach outperforms existing standard decoding strategies consistently by a clear margin. |