Papers by Jiawei Du
AssistedDS: Benchmarking How External Domain Knowledge Assists LLMs in Automated Data Science (2025.findings-emnlp)
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An Luo, Xun Xian, Jin Du, Fangqiao Tian, Ganghua Wang, Ming Zhong, Shengchun Zhao, Xuan Bi, Zirui Liu, Jiawei Zhou, Jayanth Srinivasa, Ashish Kundu, Charles Fleming, Mingyi Hong, Jie Ding
| Challenge: | Large language models (LLMs) have advanced the automation of data science workflows, yet it remains unclear whether they can critically leverage external domain knowledge as human data scientists do in practice. |
| Approach: | They propose a benchmark to evaluate how large language models handle external domain knowledge in tabular prediction tasks. |
| Outcome: | The proposed model evaluates whether it can critically leverage external domain knowledge as human data scientists do in practice. |
VPL: Visual Proxy Learning Framework for Zero-Shot Medical Image Diagnosis (2024.findings-emnlp)
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| Challenge: | Insufficient medical text precision and the modal disparity between text and vision spaces pose challenges for vision-language models like CLIP. |
| Approach: | They propose a visual proxy learning framework that combines a text refinement module and a stable Sinkhorn algorithm to enhance the diagnostic performance. |
| Outcome: | The proposed model outperforms the state-of-the-art CLIP inference by 1.69% to 15.31% on five datasets covering various diseases. |
Reverse Modeling in Large Language Models (2025.naacl-short)
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| Challenge: | Using pre-trained LLMs with reversed text inputs can improve their performance across multiple languages. |
| Approach: | They propose a way to determine whether LLMs can understand reversed text inputs by reversing entire paragraphs or documents at the token level. |
| Outcome: | The proposed model can be used to improve understanding across multiple languages. |
RESIN-11: Schema-guided Event Prediction for 11 Newsworthy Scenarios (2022.naacl-demo)
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Xinya Du, Zixuan Zhang, Sha Li, Pengfei Yu, Hongwei Wang, Tuan Lai, Xudong Lin, Ziqi Wang, Iris Liu, Ben Zhou, Haoyang Wen, Manling Li, Darryl Hannan, Jie Lei, Hyounghun Kim, Rotem Dror, Haoyu Wang, Michael Regan, Qi Zeng, Qing Lyu, Charles Yu, Carl Edwards, Xiaomeng Jin, Yizhu Jiao, Ghazaleh Kazeminejad, Zhenhailong Wang, Chris Callison-Burch, Mohit Bansal, Carl Vondrick, Jiawei Han, Dan Roth, Shih-Fu Chang, Martha Palmer, Heng Ji
| Challenge: | Existing methods for event prediction are incomplete and noisy. |
| Approach: | They propose to use news-related event schemas to extract newsworthy events . they build a demo website and include a video demonstrating the framework . |
| Outcome: | The proposed framework can be applied to a wide variety of newsworthy scenarios. |
Toward Secure Tuning: Mitigating Security Risks from Instruction Fine-Tuning (2026.acl-long)
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Yanrui Du, Fenglei Fan, Sendong Zhao, Jiawei Cao, Ming Ma, Danyang Zhao, Shuren Qi, Ting Liu, Bing Qin
| Challenge: | Instruction Fine-Tuning (IFT) has emerged as a critical technique for customizing Large Language Models (LLMs) however, recent studies have revealed that IFT can compromise the built-in security mechanisms of LLMs, posing significant security risks. |
| Approach: | They propose a method that shifts learning burden onto security-robust parameters and propose 'warm-up' phase that preferentially trains Mods_Rob to learn low-level features with minimal security risk. |
| Outcome: | The proposed method reduces security risks without sacrificing performance gains across knowledge-intensive datasets. |
ActionIE: Action Extraction from Scientific Literature with Programming Languages (2024.acl-long)
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Xianrui Zhong, Yufeng Du, Siru Ouyang, Ming Zhong, Tingfeng Luo, Qirong Ho, Hao Peng, Heng Ji, Jiawei Han
| Challenge: | a method that extracts experimental procedures from human language into actionable sequences in robotics language is challenging given the complexity of the instructions and context-dependent nature of the instruction. |
| Approach: | They propose a method that converts actions written in natural language into Python code that can be easily translated into robotics language. |
| Outcome: | The proposed method can extract experimental procedures from human language into actionable sequences in robotics language. |
DependEval: Benchmarking LLMs for Repository Dependency Understanding (2025.findings-acl)
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| Challenge: | a benchmark is designed to evaluate the repository-level dependency understanding of large language models (LLMs) based on 2683 repositories from real-world websites. |
| Approach: | They propose a benchmark to evaluate repository dependency understanding for large language models . DEPENDEVAL evaluates models on three core tasks across 8 programming languages . |
| Outcome: | The benchmark evaluates models on three core tasks across 8 programming languages from real-world repositories. |
Agent Trading Arena: A Study on Numerical Understanding in LLM-Based Agents (2025.findings-emnlp)
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| Challenge: | Existing approaches to large language models are limited to historical backtesting and static data. |
| Approach: | a new large-language model is developed to simulate real-time trading in a virtual stock market . the agent trading arena simulates real-world bid-ask interactions and provides real-life trading scenarios . |
| Outcome: | The Agent Trading Arena simulates real-world market conditions and directly impacts price dynamics. |
MedCoT: Medical Chain of Thought via Hierarchical Expert (2024.emnlp-main)
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| Challenge: | Existing methods for medical visual question answering lack robustness and reasoning paths for real-world medical diagnostics. |
| Approach: | They propose a hierarchical expert verification reasoning chain method to enhance interpretability and accuracy in medical visual question answering. |
| Outcome: | The proposed method outperforms existing methods on four standard Med-VQA datasets. |
Sparse-to-Dense: A Free Lunch for Lossless Acceleration of Video Understanding in LLMs (2025.acl-short)
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| Challenge: | Recent advances in Video Large Language Models (Video-LLMs) have achieved exceptional performance on tasks like video question answering and captioning. |
| Approach: | They propose a decoding strategy that leverages sparse top-K attention and dense full attention to accelerate Video-LLMs without loss. |
| Outcome: | The proposed approach achieves a 1.94 walltime speedup in video processing. |