时间对比解码：一种无训练方法用于大型音频-语言模型
Temporal Contrastive Decoding: A Training-Free Method for Large Audio-Language Models
AI总结：
本文提出时间对比解码方法，通过对比原始和模糊的输入路径来减少时间平滑偏差，提升音频基础输出的精确性。
摘要：
大型音频-语言模型（LALMs）能够跨语音、声音和音乐进行泛化，但统一解码器可能表现出时间平滑偏差：瞬态声学线索可能被优先考虑时间平滑的上下文所替代，导致更不具体的音频基础输出。我们提出时间对比解码（TCD），一种无训练的解码方法，用于统一的LALMs，以缓解这种影响。TCD通过平滑输入波形并重新编码来构建时间模糊的慢路径视图，然后对比原始和慢路径视图的下一个标记logits。对比信号被用作一个token级别的logit更新，仅限于一个小候选集。一个自我归一化的稳定性分数设置模糊窗口和更新规模，而基于不确定性和音频依赖性的分步门在需要时才激活更新。在MMAU和AIR-Bench上的实验显示了对强大统一LALMs的一致改进。我们进一步进行了消融分析和架构适用性研究，以分析关键组件的贡献以及TCD在大型音频-语言模型设计中的表现。
英文摘要：
Large audio-language models (LALMs) generalize across speech, sound, and music, but unified decoders can exhibit a \emph{temporal smoothing bias}: transient acoustic cues may be underutilized in favor of temporally smooth context that is better supported by language priors, leading to less specific audio-grounded outputs. We propose \emph{Temporal Contrastive Decoding} (TCD), a training-free decoding method for unified LALMs that mitigates this effect at inference time. TCD constructs a temporally blurred slow-path view by smoothing the input waveform and re-encoding it, then contrasts next-token logits from the original and slow-path views. The contrastive signal is applied as a token-level logit update restricted to a small candidate set. A self-normalized stability score sets the blur window and update scale, and a step-wise gate based on uncertainty and audio reliance activates the update only when needed. Experiments on MMAU and AIR-Bench show consistent improvements on strong unified LALMs. We further conduct ablations and an architectural applicability study to analyze the contributions of key components and how TCD behaves across large audio-language model designs.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
