Papers by Bowen Shi
Open-Domain Sign Language Translation Learned from Online Video (2022.emnlp-main)
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| Challenge: | Existing work on sign language translation has focused mainly on data collected in controlled environments or domains, which limits its applicability to real-world settings. |
| Approach: | They propose to use sign search as a pretext task and fusion of mouthing and handshape features to improve sign language translation in real-world settings. |
| Outcome: | The proposed techniques produce consistent and large improvements over baseline models based on prior work. |
Dolphin: Moving Towards Closed-loop Auto-research through Thinking, Practice, and Feedback (2025.acl-long)
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Jiakang Yuan, Xiangchao Yan, Bo Zhang, Tao Chen, Botian Shi, Wanli Ouyang, Yu Qiao, Lei Bai, Bowen Zhou
| Challenge: | Recent studies show that AI-assisted research methods can improve research efficiency . a closed-loop framework is used to enhance the automation level of scientific research . |
| Approach: | They propose a closed-loop LLM-driven framework to enhance the automation level of scientific research. |
| Outcome: | The proposed framework improves the efficiency of scientific research by improving data analysis, accelerating computation, and fostering novel idea generation. |
Profiling-Free Mixed-Precision Quantization for MoE LLMs via Fuzzy Rule Interpolation (2026.acl-long)
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| Challenge: | Large Language Models are scaling in size and capability, driving substantial computational and memory costs. |
| Approach: | They propose a mixed-precision quantization framework that uses fuzzy rule interpolation to predict quantization error from only sparse samples. |
| Outcome: | The proposed framework accelerates the profiling phase by up to 15.7 on DeepSeek-V2 while achieving comparable or slightly superior zero-shot accuracy. |
Texar: A Modularized, Versatile, and Extensible Toolkit for Text Generation (P19-3)
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Zhiting Hu, Haoran Shi, Bowen Tan, Wentao Wang, Zichao Yang, Tiancheng Zhao, Junxian He, Lianhui Qin, Di Wang, Xuezhe Ma, Zhengzhong Liu, Xiaodan Liang, Wanrong Zhu, Devendra Sachan, Eric Xing
| Challenge: | Texar is an open-source text generation toolkit that supports a broad set of text generation tasks. |
| Approach: | They introduce Texar, an open-source text generation toolkit that supports text generation tasks. |
| Outcome: | Texar supports machine translation, summarization, dialog, content manipulation, and more. |
RECALL: REpresentation-aligned Catastrophic-forgetting ALLeviation via Hierarchical Model Merging (2025.emnlp-main)
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Bowen Wang, Haiyuan Wan, Liwen Shi, Chen Yang, Peng He, Yue Ma, Haochen Han, Wenhao Li, Tiao Tan, Yongjian Li, Fangming Liu, Gong Yifan, Sheng Zhang
| Challenge: | Existing models that require task labels or performance trade-offs are susceptible to catastrophic forgetting. |
| Approach: | They propose a representation-aware model merging framework for continual learning without access to historical data. |
| Outcome: | The proposed framework outperforms baselines in knowledge retention and generalization across five NLP tasks and multiple continual learning scenarios. |
CT-FineBench: A Diagnostic Fidelity Benchmark for Fine-Grained Evaluation of CT Report Generation (2026.acl-long)
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| Challenge: | Existing evaluation metrics for radiology report generation focus on lexical overlap and entity matching. |
| Approach: | They propose a benchmark to evaluate the fine-grained factual consistency of CT reports . they use a question-answering process to query a machine-generated report . |
| Outcome: | The proposed benchmark evaluates the fine-grained factual consistency of CT reports . it correlates better with expert clinical assessment and is more sensitive to errors . |
PLAWBENCH: A Rubric-Based Benchmark for Evaluating LLMs in Real-World Legal Practice (2026.acl-long)
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Yuzhen Shi, Huanghai Liu, Yiran HU, Song Gaojie, Xu Xinran, Yubo Ma, Tianyi Tang, Li Zhang, Qingjing Chen, Feng Di, Wenbo Lv, Weiheng Wu, Kexin Yang, Sen Yang, Wei Wang, Rongyao Shi, Qiu Yuanyang, Yuemeng Qi, Zhang Jingwen, Sui Xiaoyu, Yifan Chen, Zhang Yi, An Yang, Bowen Yu, Dayiheng Liu, Junyang Lin, Weixing Shen, Bing Zhao, Charles L. A. Clarke, HU Wei
| Challenge: | Existing benchmarks for large language models (LLMs) are coarse, single-dimensional metrics and do not explicitly assess fine-grained legal reasoning. |
| Approach: | They propose a Practical Law Benchmark to evaluate large language models in real-world legal practice scenarios. |
| Outcome: | The proposed model is based on 850 questions and 13 scenarios with expert-designed evaluation rubrics. |
Edge-Enhanced Graph Convolution Networks for Event Detection with Syntactic Relation (2020.findings-emnlp)
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| Challenge: | Event detection (ED) is a key subtask of information extraction. |
| Approach: | They propose an architecture that exploits syntactic structure and typed dependency label information to perform event detection. |
| Outcome: | The proposed architecture exploits syntactic structure and typed dependency label information to perform ED. |
Prompting Large Language Models to Tackle the Full Software Development Lifecycle: A Case Study (2025.coling-main)
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Bowen Li, Wenhan Wu, Ziwei Tang, Lin Shi, John Yang, Jinyang Li, Shunyu Yao, Chen Qian, Binyuan Hui, Qicheng Zhang, Zhiyin Yu, He Du, Ping Yang, Dahua Lin, Chao Peng, Kai Chen
| Challenge: | Existing benchmarks focused on simplified or isolated aspects of coding, ignoring the full spectrum of programming challenges. |
| Approach: | They propose a case study that examines the performance of large language models across the entire software development lifecycle with four programming languages, multiple domains, and carefully designed and verified metrics for each task. |
| Outcome: | The proposed model performs across the entire software development lifecycle, including design, environment setup, implementation, acceptance testing, and unit testing. |
Searching for fingerspelled content in American Sign Language (2022.acl-long)
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| Challenge: | Sign language processing is gaining interest in the computer vision and natural language processing communities. |
| Approach: | They propose a model that jointly detects fingerspelling and matches it to a text sequence. |
| Outcome: | The proposed model outperforms baseline methods on a large public dataset of ASL fingerspelling in the wild. |
RAM-EHR: Retrieval Augmentation Meets Clinical Predictions on Electronic Health Records (2024.acl-short)
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| Challenge: | Existing deep learning models for EHRs rely on knowledge from a single source and do not capture the semantic information for medical codes. |
| Approach: | They propose a Retrieval AugMentation pipeline to augment clinical prediction on EHRs . they use multiple knowledge sources to convert them into text and use consistency regularization to capture complementary information from patient visits and summarized knowledge. |
| Outcome: | Experiments on two EHR datasets show that RAM-EHR improves clinical prediction tasks. |
XLAVS-R: Cross-Lingual Audio-Visual Speech Representation Learning for Noise-Robust Speech Perception (2024.acl-long)
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| Challenge: | Speech recognition and translation systems perform poorly on noisy inputs, which are frequent in realistic environments. |
| Approach: | They propose a cross-lingual audio-visual speech representation model for noise-robust speech recognition and translation in over 100 languages. |
| Outcome: | The proposed model outperforms the previous state-of-the-art by 18.5% WER and 4.7 BLEU on downstream audio-visual speech recognition and translation tasks. |
Towards Privacy-Aware Sign Language Translation at Scale (2024.acl-long)
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| Challenge: | Existing sign language training systems require detailed and time aligned annotations to be effective. |
| Approach: | They propose a two-stage framework for privacy-aware SLT at scale that leverages self-supervised video pretraining on anonymized and unannotated videos followed by supervised SLT finetuning on a curated parallel dataset. |
| Outcome: | The proposed framework outperforms baselines on the How2Sign dataset and achieves state-of-the-art finetuned and zero-shot gloss-free SLT performance. |
MDCure: A Scalable Pipeline for Multi-Document Instruction-Following (2025.acl-long)
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| Challenge: | Multi-document (MD) processing is crucial for LLMs to handle real-world tasks such as summarization and question-answering across large sets of documents. |
| Approach: | They propose a framework that generates high-quality synthetic MD instruction data over sets of articles via targeted prompts. |
| Outcome: | MDCure generates high-quality synthetic MD instruction data over sets of articles . evaluations show it improves over pre-trained models by up to 75.1% . |