Papers by Yawen Wu
Towards Multi-label Unknown Intent Detection (2022.coling-1)
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| Challenge: | Existing methods for multi-class unknown intent detection assume that each utterance has only one intent, which is not true because utterrances often contain multiple intents. |
| Approach: | They propose a task to detect whether an utterance contains the unknown intent by recognizing whether all intents contained in the utterant are known. |
| Outcome: | The proposed method significantly reduces the FPR95 on the MultiWOZ 2.3 dataset by 12.25% compared to the best baseline. |
On Prefix-tuning for Lightweight Out-of-distribution Detection (2023.acl-long)
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| Challenge: | Out-of-distribution (OOD) detection is a fundamental task vexing real-world applications . fine-tuning based methods require storing fine- tuned models for each scenario . |
| Approach: | They propose an unsupervised prefix-tuning based OOD detection framework called PTO . they propose to take advantage of optional training data labels and targeted OOD data . |
| Outcome: | The proposed framework performs better than existing methods under a wide range of metrics, detection settings, and OOD types. |
Debias NLU Datasets via Training-free Perturbations (2023.findings-emnlp)
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| Challenge: | Existing approaches to debiase NLU models capture biased features that are independent of the task but spuriously correlated to labels. |
| Approach: | They propose a framework that conducts training-free perturbations on samples containing biased features to Debias NLU Datasets. |
| Outcome: | The proposed framework shows competitive performance with previous state-of-the-art debiasing strategies. |
M2DF: Multi-grained Multi-curriculum Denoising Framework for Multimodal Aspect-based Sentiment Analysis (2023.emnlp-main)
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| Challenge: | Existing work mainly utilizes image information to improve the performance of MABSA task. |
| Approach: | They propose a multimodal Aspect-based Sentiment Analysis task that uses image information to improve model performance. |
| Outcome: | The proposed framework outperforms state-of-the-art work on three sub-tasks of MABSA. |
Unlocking Memorization in Large Language Models with Dynamic Soft Prompting (2024.emnlp-main)
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Zhepeng Wang, Runxue Bao, Yawen Wu, Jackson Taylor, Cao Xiao, Feng Zheng, Weiwen Jiang, Shangqian Gao, Yanfu Zhang
| Challenge: | Pretrained large language models excel in a variety of natural language processing tasks . however, they pose significant security risks due to their tendency to memorize training data . |
| Approach: | They propose a method to estimate LLM memorization using dynamic, prefix-dependent soft prompts. |
| Outcome: | The proposed method can achieve maximum relative improvement of 135.3% and 39.8% over baseline compared to state-of-the-art methods. |
SpecAgent: A Speculative Retrieval and Forecasting Agent for Code Completion (2026.acl-long)
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George Ma, Anurag Koul, Qi Chen, Yawen Wu, Sachit Kuhar, Yu Yu, Aritra Sengupta, Varun Kumar, Murali Krishna Ramanathan
| Challenge: | Large Language Models (LLMs) excel at code-related tasks but struggle in real software repositories. |
| Approach: | They propose a large-scale agent that injects repository context at inference time to improve both latency and code-generation quality by proactively exploring repository files during indexing and constructing speculative context. |
| Outcome: | Experiments show that SpecAgent achieves 9–11% relative performance gains compared to baselines while significantly reducing inference latency. |