Papers by Jun Dai
Length-Induced Embedding Collapse in PLM-based Models (2025.acl-long)
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| Challenge: | In text embeddings from PLMs are essential for many NLP applications, but performance degrades on longer texts. |
| Approach: | They propose a method which mitigates the phenomenon of Length Collapse . they propose TempScale to ensure more consistent embeddings across different text lengths . |
| Outcome: | The proposed method improves performance on MTEB and LongEmbed by 0.94% on short and 1.10% on long texts. |
Media Source Matters More Than Content: Unveiling Political Bias in LLM-Generated Citations (2025.emnlp-main)
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| Challenge: | generative search engines rely on in-line citations as the key gateway to original webpages . a recent study shows that LLMs tend to cite left-leaning sources at higher rates compared to traditional retrieval systems . |
| Approach: | They construct a dataset of news articles labeled with left- or right-leaning stances . they find that LLMs tend to cite left-leansing sources at higher rates than traditional retrieval systems . |
| Outcome: | The proposed dataset shows that LLMs tend to cite left-leaning sources at higher rates than traditional retrieval systems. |
AnyGPT: Unified Multimodal LLM with Discrete Sequence Modeling (2024.acl-long)
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Jun Zhan, Junqi Dai, Jiasheng Ye, Yunhua Zhou, Dong Zhang, Zhigeng Liu, Xin Zhang, Ruibin Yuan, Ge Zhang, Linyang Li, Hang Yan, Jie Fu, Tao Gui, Tianxiang Sun, Yu-Gang Jiang, Xipeng Qiu
| Challenge: | Existing language models that use discrete representations for unified processing of various modalities are limited to text generation and do not include multimodal output. |
| Approach: | They propose a multimodal language model that utilizes discrete representations for unified processing of various modalities. |
| Outcome: | The proposed model can be trained stably without any alterations to existing models or training paradigms. |
Cocktail: A Comprehensive Information Retrieval Benchmark with LLM-Generated Documents Integration (2024.findings-acl)
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Sunhao Dai, Weihao Liu, Yuqi Zhou, Liang Pang, Rongju Ruan, Gang Wang, Zhenhua Dong, Jun Xu, Ji-Rong Wen
| Challenge: | Large Language Models (LLMs) have led to an influx of AI-generated content on the internet, transforming corpus of Information Retrieval (IR) systems from human-written to a coexistence with LLM-generated contents. |
| Approach: | They propose a benchmark named Cocktail that compares IR models with LLMs to find relevant documents and passages from a corpus. |
| Outcome: | The proposed benchmark aims to evaluate IR models in the mixed-sourced data landscape of the LLM era. |
Compensate Quantization Errors: Make Weights Hierarchical to Compensate Each Other (2024.findings-naacl)
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| Challenge: | Emergent Large Language Models (LLMs) use extraordinary performance and powerful deduction capacity to discern from traditional language models. |
| Approach: | They propose a method that uses weights to compensate quantization error and learnable singular value incremental (LSI) LSI is a technique that helps weights compensate each other conditioned on activation. |
| Outcome: | The proposed method achieves state-of-the-art performance in diverse quantization settings, no matter in weight-only, weight-activation or extremely low bit scenarios. |
Boost, Disentangle, and Customize: A Robust System2-to-System1 Pipeline for Code Generation (2025.findings-acl)
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Kounianhua Du, Hanjing Wang, Jianxing Liu, Jizheng Chen, Xinyi Dai, Yasheng Wang, Ruiming Tang, Yong Yu, Jun Wang, Weinan Zhang
| Challenge: | Existing systems 2 methods for code generation are difficult to implement due to the complex hidden reasoning process and heterogeneous data distribution. |
| Approach: | They propose a framework that Boosts reasoning exploration via multi-agent collaboration and Disentangles heterogeneous data into specialized experts. |
| Outcome: | The proposed framework outperforms state-of-the-art methods on APPS and CodeContest benchmarks and achieves 73.8% accuracy on hard problems. |
ControlAudio: Tackling Text-Guided, Timing-Indicated and Intelligible Audio Generation via Progressive Diffusion Modeling (2026.acl-long)
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| Challenge: | Recent efforts on text-to-audio generation are exploring fine-grained controllability . however, their performance at scale is limited due to data scarcity . |
| Approach: | They propose a multi-task learning problem for high-controllability text-to-audio generation . they propose scalable diffusion transformers that augment condition information in sequence . |
| Outcome: | The proposed method outperforms existing methods on objective and subjective evaluations. |
A Reinforced Generation of Adversarial Examples for Neural Machine Translation (2020.acl-main)
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| Challenge: | Neural machine translation systems fail on less decent inputs, which may harm the credibility of these systems. |
| Approach: | They propose a paradigm that generates adversarial examples using reinforcement learning to expose pitfalls for a given performance metric. |
| Outcome: | The proposed paradigm produces stable attacks with meaning-preserving adversarial examples. |
CARD: Cross-modal Agent Framework for Generative and Editable Residential Design (2025.emnlp-main)
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| Challenge: | Architectural design automation has made significant progress, but the complexity of open-world environments makes residential design a challenging task. |
| Approach: | They propose a framework that leverages a system of specialized cross-modal agents to adapt to open-world residential design. |
| Outcome: | The proposed framework enables users to generate and edit residential design without requiring specialized expertise. |
FloorPlan-LLaMa: Aligning Architects’ Feedback and Domain Knowledge in Architectural Floor Plan Generation (2025.acl-long)
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| Challenge: | Existing evaluation methods for floor plan generation rely on statistical metrics like FID, GED, and PSNR, which fail to evaluate using domain knowledge. |
| Approach: | They propose to use a first floor plan dataset to train a floor plan generation model based on a multi-dimensional preference score and a textual analysis to integrate architects’ professional expertise and preferences. |
| Outcome: | The proposed model outperforms baseline models in text-conditional and class-condition tasks and is more rational and aligns better with human preferences. |
PRA-RAG: Provably Robust Aggregation in Retrieval-Augmented Generation against Retrieval Corruption (2026.findings-acl)
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Xue Tan, Yi Zheng, Chang Huo, Yunruo Zhang, Yu Liu, Hao Luan, Zhuyang Yu, Jun Dai, Xiaoyan Sun, Ping Chen
| Challenge: | Existing defense mechanisms lack theoretical robustness guarantees and perform unreliably when the LLM has limited knowledge of the retrieved content. |
| Approach: | They propose a provably robust retrieval aggregation algorithm designed to defend against poisoning attacks on retrieved texts. |
| Outcome: | Experiments show that PRA-RAG reduces the attack success rate to as low as 1% while maintaining an accuracy of 71%, significantly outperforming representative state-of-the-art (SOTA) methods. |
CitaLaw: Enhancing LLM with Citations in Legal Domain (2025.findings-acl)
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| Challenge: | Existing benchmarks have focused on enabling large language models (LLMs) to generate citationsupported outputs. |
| Approach: | They propose to use a citation-based framework to evaluate LLMs' ability to produce legally sound responses with appropriate citations. |
| Outcome: | The proposed framework enables LLMs to retrieve supporting citations from the reference corpus and align these citation with the corresponding sentences in their responses. |
MatchTIR: Fine-Grained Supervision for Tool-Integrated Reasoning via Bipartite Matching (2026.acl-long)
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| Challenge: | Existing reinforcement learning methods rely on outcome- or trajectory-level rewards, assigning uniform advantages to all steps within a trajectory. |
| Approach: | They propose a framework that introduces fine-grained supervision via bipartite matching-based turn-level reward assignment and dual-level advantage estimation. |
| Outcome: | The proposed framework surpasses the majority of 8B competitors on three benchmarks. |
Uplift-RAG: Uplift-Driven Knowledge Preference Alignment for Retrieval-Augmented Generation (2025.findings-emnlp)
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| Challenge: | Existing efforts to estimate document utility rely on downstream generation performance, which conflates the influence of external documents with the intrinsic knowledge of the LLM. |
| Approach: | They propose an uplift-based definition of document utility that quantifies each document’s marginal benefit over the LLM’s internal knowledge. |
| Outcome: | The proposed framework improves the performance of the LLM by incorporating external retrieved documents into the model. |
Towards Interpretable Clinical Diagnosis with Bayesian Network Ensembles Stacked on Entity-Aware CNNs (2020.acl-main)
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| Challenge: | a novel framework for text-based diagnosis of diseases requires appropriate balance between accuracy and interpretability. |
| Approach: | They propose a framework that stacks Bayesian Network Ensembles on top of CNN to build an accurate yet interpretable diagnosis system. |
| Outcome: | The proposed framework outperforms the previous automatic diagnosis methods in accuracy performance and the diagnosis explanation of the framework is reasonable. |
RevPRAG: Revealing Poisoning Attacks in Retrieval-Augmented Generation through LLM Activation Analysis (2025.findings-emnlp)
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| Challenge: | Retrieval-Augmented Generation (RAG) enriches the input to LLMs by retrieving information from the relevant knowledge database. |
| Approach: | They propose to use a knowledge database to enrich the input of LLMs by retrieving information from the relevant knowledge database. |
| Outcome: | The proposed approach can achieve 98% true positive rate while maintaining a false positive rate close to 1%. |
Towards Text-Image Interleaved Retrieval (2025.acl-long)
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Xin Zhang, Ziqi Dai, Yongqi Li, Yanzhao Zhang, Dingkun Long, Pengjun Xie, Meishan Zhang, Jun Yu, Wenjie Li, Min Zhang
| Challenge: | Existing multimodal information retrieval models rely on single-image inputs . current models use a dense retrieval paradigm, but this approach is not effective . |
| Approach: | They propose a text-image interleaved retrieval task where query and document are interleaves . they adapt off-the-shelf retrievers and build a dense baseline by interleaded multimodal large language model . |
| Outcome: | The proposed model achieves significant improvements over the baseline by substantially fewer visual tokens. |