Deeper is Not Always Better: Mitigating the Alignment Tax via Confident Layer Decoding
Autoregressive generation in large language models traditionally uses the final layer for token prediction, but a new decoding strategy dynamically selects more reliable intermedia…
Hugging Face · Daily Papers
·Xuanming Zhang, Sining Zhoubian
·
·▲ 9 upvotes
Este artigo está em destaque na seleção diária de papers do Hugging Face, curada pela comunidade de pesquisa em IA.
Autores: Xuanming Zhang, Sining Zhoubian, Yuxuan Chen, Tianyi Tang, An Yang, Sean Du
- 9 upvotes da comunidade
- Temas: autoregressive generation, large language models, next-token predictions, Guess-Refine-Perturb dynamic, confident decoding, entropy-guided conservative backward search
Resumo
Resumo original (em inglês), extraído do paper:
Autoregressive generation in large language models traditionally uses the final layer for token prediction, but a new decoding strategy dynamically selects more reliable intermediate layers based on entropy-guided search, improving reasoning performance with minimal computational overhead.
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