Papers by Shuai Tang
WhiteningBERT: An Easy Unsupervised Sentence Embedding Approach (2021.findings-emnlp)
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| Challenge: | Pre-trained language models perform well on learning sentence semantics when fine-tuned with supervised data. |
| Approach: | They conduct a thorough examination of pretrained model based unsupervised sentence embeddings. |
| Outcome: | The proposed approach improves on whitening-based vector normalization with less than 10 lines of code. |
A Deep Learning-Based System for PharmaCoNER (D19-57)
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Ying Xiong, Yedan Shen, Yuanhang Huang, Shuai Chen, Buzhou Tang, Xiaolong Wang, Qingcai Chen, Jun Yan, Yi Zhou
| Challenge: | Efficient access to mentions of clinical entities is very important for using clinical text. |
| Approach: | They developed a pipeline system based on deep learning methods for this shared task . it achieves a micro-average F1-score of 0.9105 on track 1 and a mini-average LSTM score of 0.8391 on track 2 . |
| Outcome: | The proposed system achieves a micro-average F1-score of 0.9105 on track 1 and a mini-average score of 0.8391 on track 2. |
Taylor Unswift: Secured Weight Release for Large Language Models via Taylor Expansion (2024.emnlp-main)
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Guanchu Wang, Yu-Neng Chuang, Ruixiang Tang, Shaochen Zhong, Jiayi Yuan, Hongye Jin, Zirui Liu, Vipin Chaudhary, Shuai Xu, James Caverlee, Xia Hu
| Challenge: | Existing mechanisms compromise ownership rights or raise data privacy concerns . existing mechanisms compromise security of released large language models . |
| Approach: | They propose a TaylorMLP to preserve the ownership of large language models by transforming the weights of LLMs into Taylor-series parameters instead of releasing original weights . |
| Outcome: | The proposed model preserves ownership of large language models and prevents their abuse by adjusting the generation speed and causing low-speed token generation. |
GeoDRL: A Self-Learning Framework for Geometry Problem Solving using Reinforcement Learning in Deductive Reasoning (2023.findings-acl)
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| Challenge: | Existing methods for automated geometry problem solving lack labeled data. |
| Approach: | They propose a framework that integrates logic graph deduction and deep reinforcement learning to optimize geometry reasoning as a Markov Decision Process. |
| Outcome: | The proposed framework improves accuracy and interpretability in the Geometry3K dataset while maintaining correctness. |
A3: Android Agent Arena for Mobile GUI Agents with Essential-State Procedural Evaluation (2026.findings-acl)
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Yuxiang Chai, Shunye Tang, Han Xiao, Weifeng Lin, Hanhao Li, Jiayu Zhang, Liang Liu, Pengxiang Zhao, Guangyi Liu, Guozhi Wang, Shuai Ren, Rongduo Han, Haining Zhang, Siyuan Huang, Hongsheng Li
| Challenge: | Existing evaluation methods for mobile GUI agents rely on static frame assessments or offline static apps. |
| Approach: | They propose an evaluation system that leverages large language models as reward models to verify task completion and process achievement. |
| Outcome: | The proposed system addresses the limitations of traditional function based evaluation methods on online dynamic apps. |
Learning Retrieval Augmentation for Personalized Dialogue Generation (2023.emnlp-main)
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| Challenge: | Personalized dialogue generation is a popular approach for conversational AI applications . however, persona profiles may not provide comprehensive descriptions of the persona . |
| Approach: | They propose a method that leverages persona profiles and dialogue context to generate personalized dialogues by leveraging personas and persona profile. |
| Outcome: | The proposed method outperforms baselines on the CONVAI2 dataset . it is expected to generate personalized dialogues based on persona profiles and dialogue context . |
Exploiting Invertible Decoders for Unsupervised Sentence Representation Learning (P19-1)
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| Challenge: | Encoder-decoder models for unsupervised sentence representation learning discard decoder after training . decoded sentences are often used to make better predictions of words in a given sentence . |
| Approach: | They propose two types of decoding functions whose inverse can be easily derived without expensive inverse calculation. |
| Outcome: | The proposed models can learn good representations from encoders and decoders without expensive calculations. |