Papers by Qilin Wang
Estimating the Black-box LLM Uncertainty with Distribution-Aligned Adversarial Distillation (2026.acl-long)
Copied to clipboard
| Challenge: | Existing uncertainty quantification methods depend on computationally expensive multiple sampling or internal parameters, which prevents real-time estimation and fails to capture information implicit in the black-box reasoning process. |
| Approach: | They propose a distribution-aligned adjudication architecture to guide a lightweight proxy model to learn the high-quality regions of the output distribution of the black-box LLM. |
| Outcome: | Extensive experiments show that a proxy model even with 1% of the target LLM’s size can achieve reliable uncertainty quantification. |
Towards Fine-grained Audio Captioning with Multimodal Contextual Fusion (2026.acl-long)
Copied to clipboard
| Challenge: | Existing methods for audio captioning lack fine-grained detail and contextual accuracy due to limited unimodal or superficial information. |
| Approach: | They propose a two-stage automated pipeline that uses pretrained models to extract contextual cues from video . a large language model synthesizes these inputs to generate detailed and context-aware captions . |
| Outcome: | The proposed method is scalable and generates detailed and context-aware captions on large-scale audio datasets. |