Papers by Shijie Chen
RAG-Star: Enhancing Deliberative Reasoning with Retrieval Augmented Verification and Refinement (2025.naacl-long)
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| Challenge: | Existing large language models (LLMs) show exceptional problem-solving capabilities but struggle with complex reasoning tasks. |
| Approach: | They propose a novel RAG approach that integrates retrieved information to guide tree-based reasoning process based on LLMs. |
| Outcome: | The proposed approach outperforms existing methods in large language models . iteratively plans intermediate sub-queries and answers based on the LLM itself . |
A High-Quality Text-Rich Image Instruction Tuning Dataset via Hybrid Instruction Generation (2025.coling-main)
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| Challenge: | Large multimodal models struggle with text-rich images because of inadequate training data. |
| Approach: | They propose to use annotations from human annotators to generate instruction data by a hybrid approach to generate text prompts for large language models. |
| Outcome: | The proposed model improves multimodal alignment for text-rich images by using human annotations and tailored text prompts for large language models. |
Joint Semantic and Strategy Matching for Persuasive Dialogue (2023.findings-emnlp)
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Chuhao Jin, Yutao Zhu, Lingzhen Kong, Shijie Li, Xiao Zhang, Ruihua Song, Xu Chen, Huan Chen, Yuchong Sun, Yu Chen, Jun Xu
| Challenge: | Persuasive dialogue models rely on utterance semantic matching and a key aspect has been ignored . compared with utterrance semantics, conversation strategies are high-level concepts, which can be informative and provide complementary information to achieve effective persuation. |
| Approach: | They propose to model conversation semantics and strategies to match them using a BERT-like module and an auto-regressive predictor. |
| Outcome: | The proposed model improves state-of-the-art by 5% on a small and 37% on 'large' datasets. |
Rectified Sparse Attention for Efficient Long-Sequence Generation (2026.findings-acl)
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Yutao Sun, Tianzhu Ye, Li Dong, Yuqing Xia, Jian Chen, Yizhao Gao, Shijie Cao, Jianyong Wang, Furu Wei
| Challenge: | Recent sparse decoding methods improve efficiency but suffer from KV cache misalignment, resulting in performance degradation. |
| Approach: | They propose a method that combines block-sparse attention with periodic dense rectification to bound error accumulation and preserve alignment with the pretraining distribution. |
| Outcome: | Experiments on math reasoning, language modeling, and retrieval tasks show that ReSA achieves near-lossless generation quality with significantly improved efficiency. |
SwarmAgentic: Towards Fully Automated Agentic System Generation via Swarm Intelligence (2025.emnlp-main)
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| Challenge: | Existing agentic system generation frameworks lack autonomy, autonomy, and functionality . current frameworks are too rigid, limiting adaptability and scalability. |
| Approach: | They propose a framework that fully automates agentic system generation, optimization, and collaboration . they construct agents from scratch and jointly refine functionality and coordination . |
| Outcome: | The proposed framework outperforms ADAS on six real-world, open-ended, and exploratory tasks on the TravelPlanner benchmark. |
Unveiling Inherent Visual Grounding in Multimodal LLMs for Text-Rich Images (2026.findings-acl)
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Shijie Zhou, Jihyung Kil, Ming Li, Jiuxiang Gu, Curtis Wigington, Rajiv Jain, Changyou Chen, Ruiyi Zhang
| Challenge: | Existing multimodal large language model (MLLM) approaches struggle to align query tokens with visual–text patches, heavily relying on lengthy OCR inputs. |
| Approach: | They propose an OCR-free approach that leverages the MLLM's inherent multi-head attention for multi-patch grounding. |
| Outcome: | Empirical results show that the proposed approach outperforms existing approaches on challenging document grounding benchmarks. |
Completing A Systematic Review in Hours instead of Months with Interactive AI Agents (2025.acl-long)
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| Challenge: | Systematic reviews (SRs) are vital for evidence-based practice in high stakes disciplines, such as healthcare. |
| Approach: | They propose a human-centered interactive AI agent powered by large language models that partitions a large literature corpus based on semantics and employs . |
| Outcome: | InsightAgent improves quality of synthesized SRs by 27.2%, reaching 79.7% of human-written quality. |
Simple-VGC: Enhancing Visual Grounding in Multimodal Reasoning via Adaptive Tool Composition (2026.acl-long)
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| Challenge: | Existing multimodal large language models suffer from systematic failures in basic visual understanding. |
| Approach: | They propose a tool-augmented reasoning framework with three targeted compensation strategies to address these problems. |
| Outcome: | The proposed framework improves visual grounding by re-injecting the original image to mitigate visual forgetting, the authors show . the proposed framework also improves the accuracy of the visual inputs, the researchers show - and the results are promising . |
Thought-Action Graph Reasoning: Faithful and Efficient Reasoning of Large Language Models via Reusing Past Experience (2026.findings-acl)
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Zhixiao Qi, Feng Huang, Yunqi Zhang, Shijie Zhang, Qingqing Sun, Yongfeng Huang, Minghu Jiang, Shuai Chen, Tianyi Zhang
| Challenge: | Existing methods for integrating knowledge graphs with LLMs suffer from poor generalization or low reasoning efficiency. |
| Approach: | They propose a thought-action Graph (TAG) that decomposes LLM-KG interaction trajectories into fine-grained semantic operators and guides LLM to execute on them. |
| Outcome: | The proposed paradigm outperforms state-of-the-art methods on KGQA benchmarks while reducing the number of LLM calls and generated tokens. |
Sticker-TTS: Learn to Utilize Historical Experience with a Sticker-driven Test-Time Scaling Framework (2025.emnlp-main)
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| Challenge: | Large reasoning models have exhibited strong performance on complex reasoning tasks, but current test-time scaling methods rely on redundant sampling and ignore historical experience utilization. |
| Approach: | They propose a test-time scaling framework that coordinates three collaborative LRMs to iteratively explore and refine solutions guided by historical attempts. |
| Outcome: | The proposed framework surpasses strong baselines on three mathematical reasoning benchmarks, including AIME-24, AIME-25, and OlymMATH. |
Text-to-SQL Error Correction with Language Models of Code (2023.acl-short)
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Ziru Chen, Shijie Chen, Michael White, Raymond Mooney, Ali Payani, Jayanth Srinivasa, Yu Su, Huan Sun
| Challenge: | Existing semantic parsers are not accurate enough for use in text-to-SQL parsing tasks. |
| Approach: | They propose to build clause-level edit models to correct SQL queries instead of token-level ones. |
| Outcome: | The proposed model improves the exact set match accuracy of different parsers by 2.4-6.5 and obtains up to 4.3 point absolute improvement over two strong baselines. |
AutoSDT: Scaling Data-Driven Discovery Tasks Toward Open Co-Scientists (2025.emnlp-main)
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Yifei Li, Hanane Nour Moussa, Ziru Chen, Shijie Chen, Botao Yu, Mingyi Xue, Benjamin Burns, Tzu-Yao Chiu, Vishal Dey, Zitong Lu, Chen Wei, Qianheng Zhang, Tianyu Zhang, Song Gao, Xuhui Huang, Xia Ning, Nesreen K. Ahmed, Ali Payani, Huan Sun
| Challenge: | AutoSDT-5K is the only automatically collected and the largest open dataset for data-driven scientific discovery. |
| Approach: | They propose an automatic pipeline that collects high-quality coding tasks in real-world data-driven discovery workflows. |
| Outcome: | The proposed pipeline synthesizes accurate tasks and tasks from a dataset of 5,404 tasks covering four scientific disciplines and 756 Python packages. |
QA‐LIGN: Aligning LLMs through Constitutionally Decomposed QA (2025.findings-emnlp)
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Jacob Dineen, Aswin Rrv, Qin Liu, Zhikun Xu, Xiao Ye, Ming Shen, Zhaonan Li, Shijie Lu, Chitta Baral, Muhao Chen, Ben Zhou
| Challenge: | QA-LIGN decomposes monolithic rewards into interpretable principle-specific evaluations . scalar rewards obscure which objectives drive the training signal . |
| Approach: | a new method decomposes monolithic rewards into interpretable principle-specific evaluations . QA-LIGN reduces attack success rates by up to 68.7% while maintaining a 0.67% false refusal rate . |
| Outcome: | QA-LIGN reduces attack success rates by up to 68.7% while maintaining a 0.67% false refusal rate . the results outperform DPO and GRPO with state-of-the-art reward models given equivalent training . |
Everything Is All It Takes: A Multipronged Strategy for Zero-Shot Cross-Lingual Information Extraction (2021.emnlp-main)
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Mahsa Yarmohammadi, Shijie Wu, Marc Marone, Haoran Xu, Seth Ebner, Guanghui Qin, Yunmo Chen, Jialiang Guo, Craig Harman, Kenton Murray, Aaron Steven White, Mark Dredze, Benjamin Van Durme
| Challenge: | Zero-shot cross-lingual information extraction (IE) is a technique for training data in a source language but not in . |
| Approach: | They explore techniques including data projection and self-training to improve zero-shot cross-lingual information extraction (IE) IE is a construction of an IE model for some target language given existing annotations exclusively in English. |
| Outcome: | The proposed techniques show that they perform better than any single strategy. |
Error Detection for Text-to-SQL Semantic Parsing (2023.findings-emnlp)
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| Challenge: | Existing text-to-SQL parsers are often over-confident, thus casting doubt on their trustworthiness when deployed for real use. |
| Approach: | They propose a parser-independent error detection model for text-to-SQL semantic parsing . they use a language model of code as its bedrock and graph neural networks to learn structural features of queries . |
| Outcome: | The proposed model outperforms parser-dependent uncertainty metrics on three strong parsers . it could improve the performance and usability of text-to-SQL semantic parsing, it is shown . |