Papers by Qin Dai
Denoising Bottleneck with Mutual Information Maximization for Video Multimodal Fusion (2023.acl-long)
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| Challenge: | Prior denoising methods suppress redundant and noisy information at risk of losing critical information. |
| Approach: | They propose a denoising bottleneck fusion model for fine-grained video multimodal fusion . they employ a bottleneck mechanism to filter out noise and redundancy with a restrained receptive field . |
| Outcome: | The proposed model improves on state-of-the-art video multimodal fusion benchmarks. |
Tool Zero: Training Tool-Augmented LLMs via Pure RL from Scratch (2025.findings-emnlp)
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Yirong Zeng, Xiao Ding, Yutai Hou, Yuxian Wang, Li Du, Juyi Dai, Qiuyang Ding, Duyu Tang, Dandan Tu, Weiwen Liu, Bing Qin, Ting Liu
| Challenge: | Experimental results demonstrate that our models achieve over 7% performance improvement compared to both SFT and RL-with-SFT models under the same experimental settings. |
| Approach: | They propose a dynamic generalization-guided reward design for rule-based RL that shifts rewards from exploratory to exploitative tool-use patterns. |
| Outcome: | The proposed model achieves over 7% performance improvement compared to SFT and RL-with-SFT models under the same experimental settings. |
Cross-stitching Text and Knowledge Graph Encoders for Distantly Supervised Relation Extraction (2022.emnlp-main)
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| Challenge: | Existing bi-encoder architectures do not allow any sharing between text and knowledge graphs . john sutter: experimental results show that enabling full interaction yields strong improvements. |
| Approach: | They propose cross-stitch bi-encoders that allow full interaction between text and KG . they say the amount of sharing is dynamically controlled via cross-attention-based gates . |
| Outcome: | Experimental results show that bi-encoder architectures yield strong improvements . cross-stitch mechanism allows sharing and updating representations between two encoders . |
Two Training Strategies for Improving Relation Extraction over Universal Graph (2021.eacl-main)
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| Challenge: | Existing methods for Distantly Supervised Relation Extraction (DS-RE) with a UG may lead to degradation in performance. |
| Approach: | They propose to use a Universal Graph (UG) to train a distantly supervised relation extraction model. |
| Outcome: | The proposed training strategies on biomedical and NYT10 datasets prove the robustness of the proposed methods and achieve state-of-the-art results. |
Bridging Reasoning and Action: Hybrid LLM–RL Framework for Efficient Cross-Domain Task-Oriented Dialogue (2026.findings-acl)
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| Challenge: | Existing methods to solve cross-domain task-oriented dialogues are brittle when cross- domain constraints are not directly grounded in surface text or require commonsense inference. |
| Approach: | They propose a framework that makes LLM-derived constraint reasoning usable for RL. |
| Outcome: | Experiments show that the proposed framework outperforms single-model baselines on long-horizon tasks. |
Representational Analysis of Binding in Language Models (2024.emnlp-main)
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| Challenge: | Existing research has shown that LMs use a concept called Binding ID (BI) to mark entity-attribute pairs, but have not captured the information from entity activations. |
| Approach: | They propose to localize the Binding ID mechanism by localizing BI information in LMs by encoding it in a low-rank subspace. |
| Outcome: | The proposed model can infer attributes for a given entity from a container . |
UniDrop: A Simple yet Effective Technique to Improve Transformer without Extra Cost (2021.naacl-main)
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| Challenge: | Existing approaches to improve the performance of natural language processing models are over-parameterized and overfitted. |
| Approach: | They propose an approach to integrate dropout techniques into the training of Transformer models. |
| Outcome: | The proposed approach can achieve 1.5 BLEU improvement on IWSLT14 translation tasks and better accuracy for the classification even using strong pre-trained RoBERTa as backbone. |
Cell-Based Representation of Relational Binding in Language Models (2026.acl-long)
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| Challenge: | Recent work has found evidence that Large Language Models (LLMs) are able to track entities across discourse . however, the mechanism by which they bind entities, relations, and attributes remains unclear . |
| Approach: | They propose a low-dimensional cell-based binding representation for relational binding . they also show that context-specific CBR representations are related by translation vectors . |
| Outcome: | The proposed model encodes a low-dimensional cell-based binding representation (CBR) a translation vector in activation space enables cross-context transfer, the study shows . |
MessToClean: Evidence-Grounded Structure-Preserving Reconstruction for Real-World Degraded Exam Paper Images (2026.acl-long)
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Jiayi Tuo, Cheng Tang, Zihan Wang, Chenyue Zhou, Yao Li, Yanbiao Ma, Chao Wang, Wei Dai, Mingxuan Wang, Shitong Qin, Ziwei Zhao
| Challenge: | Existing Multimodal Large Language Models (MLLMs) fail under RDEI, leading to disrupted structure and evidence-unsupported hallucinations. |
| Approach: | They propose a backbone-agnostic, evidence-driven pipeline that treats off-the-shelf MLLMs as interchangeable components to improve stem consistency and figure consistency. |
| Outcome: | The proposed pipeline improves stem consistency by 1.01-3.18%, figure consistency by 0.50-49.16%, and refusal F1 by 1.06-10.88% across question types. |
PaRaDe: Passage Ranking using Demonstrations with LLMs (2023.findings-emnlp)
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Andrew Drozdov, Honglei Zhuang, Zhuyun Dai, Zhen Qin, Razieh Rahimi, Xuanhui Wang, Dana Alon, Mohit Iyyer, Andrew McCallum, Donald Metzler, Kai Hui
| Challenge: | Existing studies show that large language models can be instructed to perform zero-shot passage re-ranking . Existing work like UPR demonstrate promising results for zero- shot ranking using LLMs . |
| Approach: | They propose a demonstration selection strategy based on difficulty rather than semantic similarity . they propose to include only one demonstration in the prompt to improve re-ranking . |
| Outcome: | The proposed method improves LLM-based re-ranking by adding one demonstration to the prompt. |
Self-Reflective Generation at Test Time (2026.acl-long)
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| Challenge: | Existing self-reflection mechanisms are reactive and inefficient for large language models . a fundamental tension persists between the ability to execute complex multi-step reasoning and the ability of the model to generate coherent traces. |
| Approach: | They propose a test-time framework that reflects before generating at uncertain points . SRGen utilizes dynamic entropy thresholding to identify high-uncertainty tokens . |
| Outcome: | The proposed framework can significantly strengthen large language models' reasoning process. |