Papers by Yidi Li
UniCodec: Unified Audio Codec with Single Domain-Adaptive Codebook (2025.acl-long)
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Yidi Jiang, Qian Chen, Shengpeng Ji, Yu Xi, Wen Wang, Chong Zhang, Xianghu Yue, ShiLiang Zhang, Haizhou Li
| Challenge: | Existing neural audio codecs are not capable of handling multi-domain audio data . et al., 2023) integrate speech modality with text-based large language models . |
| Approach: | They propose a unified audio codec with a single codebook to support multi-domain audio data . they propose combining a mix-of-experts strategy and a partitioned domain-adaptive codebook method . |
| Outcome: | The proposed codec outperforms existing codecs on acoustic and semantic representation capabilities. |
Firm or Fickle? Evaluating Large Language Models Consistency in Sequential Interactions (2025.findings-acl)
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| Challenge: | Large Language Models (LLMs) have demonstrated remarkable capabilities across diverse tasks, but their deployment in high-stake domains requires consistent and coherent behavior across multiple rounds of user interaction. |
| Approach: | They propose a framework for evaluating and improving LLM response consistency, and introduce a benchmark dataset to evaluate LLM consistency. |
| Outcome: | The proposed framework improves response stability without sacrificing accuracy, and offers a practical path toward more dependable behavior in critical, real-world deployments. |
Diffusion-CAM: Faithful Visual Explanations for dMLLMs (2026.acl-long)
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| Challenge: | Existing Class Activation Mapping methods are ill-suited for interpreting non-autoregressive behaviors of diffusion-based architectures. |
| Approach: | They propose to use a method to generate parallel activation maps by probing intermediate representations in the transformer backbone to capture latent features and their class-specific gradients. |
| Outcome: | Experiments show that Diffusion-CAM significantly outperforms SoTA methods in localization accuracy and visual fidelity. |