Papers by Wenjie Zhang

Unveiling the Key Factors for Distilling Chain-of-Thought Reasoning (2025.findings-acl)

Challenge:	Existing approaches to detect fake news in unseen domains are limited by domain-specific training.
Approach:	They propose a cross-domain fake news detection method based on adversarial training . they use a document-level and entity-level model to generate domain-independent representations .
Outcome:	The proposed method can detect fake news in unseen domains with the help of pre-trained language models.

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Xinghao Chen, Zhijing Sun, Guo Wenjin, Miaoran Zhang, Yanjun Chen, Yirong Sun, Hui Su, Yijie Pan, Dietrich Klakow, Wenjie Li, Xiaoyu Shen

mGTE: Generalized Long-Context Text Representation and Reranking Models for Multilingual Text Retrieval (2024.emnlp-industry)

Challenge:	Large Language Models (LLMs) excel in reasoning tasks through Chain-of-Thought prompting.
Approach:	They examine the factors influencing CoT distillation including granularity, format and teacher model.
Outcome:	The proposed model is based on four teacher models and seven student models across seven mathematical and commonsense reasoning datasets.

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Xin Zhang, Yanzhao Zhang, Dingkun Long, Wen Xie, Ziqi Dai, Jialong Tang, Huan Lin, Baosong Yang, Pengjun Xie, Fei Huang, Meishan Zhang, Wenjie Li, Min Zhang

Hypothetical Training for Robust Machine Reading Comprehension of Tabular Context (2023.findings-acl)

Challenge:	Existing models for text retrieval are based on a multi-stage process that involves retrieving documents from a large corpus.
Approach:	They propose to build a multilingual text representation model and a cross-encoder reranker from scratch for text retrieval.
Outcome:	The proposed models outperform the state-of-the-art models on long-context retrieval benchmarks.

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Moxin Li, Wenjie Wang, Fuli Feng, Hanwang Zhang, Qifan Wang, Tat-Seng Chua

PERM: Psychology-grounded Empathetic Reward Modeling for Large Language Models (2026.findings-acl)

Challenge:	Counterfactual training is expensive because of the complexity of tabular data.
Approach:	They propose a hypothetical training framework that uses paired examples with different hypothetical questions to supervise the direction of model gradient towards the counterfactual answer change.
Outcome:	The proposed framework improves on tabular MRC datasets.

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Chengbing Wang, Wuqiang Zheng, Yang Zhang, Fengbin Zhu, Junyi Cheng, Yi Xie, Wenjie Wang, Fuli Feng

Personalized Text Generation with Contrastive Activation Steering (2025.acl-long)

Challenge:	Existing reward models evaluate empathy from a single perspective, overlooking bidirectional interaction nature of empathy.
Approach:	They propose a reward model that evaluates empathy from a single perspective . they propose PERM to integrate a bystander perspective to monitor overall interaction quality .
Outcome:	a new reward model outperforms state-of-the-art models on an emotional intelligence benchmark and an industrial daily conversation dataset.

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Jinghao Zhang, Yuting Liu, Wenjie Wang, Qiang Liu, Shu Wu, Liang Wang, Tat-Seng Chua

Information Retrieval

Challenge:	Existing approaches to personalized text generation rely on retrieval-augmented generation and parameter-efficient fine-tuning.
Approach:	They propose a training-free framework that disentangles and represents personalized writing style as a vector in LLM’s activation-space.
Outcome:	The proposed framework achieves 8% relative improvement in personalized generation while reducing storage requirements by 1700 over PEFT method.

MolTC: Towards Molecular Relational Modeling In Language Models (2024.findings-acl)

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Junfeng Fang, Shuai Zhang, Chang Wu, Zhengyi Yang, Zhiyuan Liu, Sihang Li, Kun Wang, Wenjie Du, Xiang Wang

Training Turn-by-Turn Verifiers for Dialogue Tutoring Agents: The Curious Case of LLMs as Your Coding Tutors (2025.findings-acl)

Challenge:	Molecular Relational Learning (MRL) is a promising way to understand interactions between molecular pairs.
Approach:	They propose a novel LLM-based multi-modal framework for molecular interaction modeling following Chain-of-Thought (CoT) theory which integrates graphical information of two molecules in pair.
Outcome:	The proposed framework integrates graphical information of two molecules in pair.

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Jian Wang, Yinpei Dai, Yichi Zhang, Ziqiao Ma, Wenjie Li, Joyce Chai

Natural Language Interfaces

Challenge:	Existing studies have focused on coding tutoring, but their capabilities in guiding users to solve complex tasks remain underexplored.
Approach:	They propose a novel agent workflow, Trace-and-Verify, which combines knowledge tracing to estimate a student’s knowledge state and turn-by-turn verification to ensure effective guidance toward task completion.
Outcome:	The proposed agent workflow achieves significantly higher success rates than existing tutoring agents.

MulDimIF: A Multi-Dimensional Constraint Framework for Evaluating and Improving Instruction Following in Large Language Models (2026.findings-acl)

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Junjie Ye, Caishuang Huang, Zhuohan Chen, Wenjie Fu, Chenyuan Yang, Leyi Yang, Yilong Wu, Peng Wang, Meng Zhou, Xiaolong Yang, Tao Gui, Qi Zhang, Zhongchao Shi, Jianping Fan, Xuanjing Huang

Multimodal Cross-lingual Phrase Retrieval (2024.lrec-main)

Challenge:	Existing research has focused on constraint categories, offering little guidance for improving instruction following abilities.
Approach:	They propose a multi-dimensional constraint framework that allows for instruction following . they construct 9,106 code-verifiable samples and evaluate 18 LLMs .
Outcome:	The proposed framework improves instruction following performance without compromising general performance.

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Chuanqi Dong, Wenjie Zhou, Xiangyu Duan, Yuqi Zhang, Min Zhang

Question-type Driven Question Generation (D19-1)

Challenge:	Existing approaches to cross-lingual phrase retrieval only deal with textual modality, leaving the question of the effectiveness of using multimodal information unanswered.
Approach:	They propose a multimodal cross-lingual phrase retrieval resource that integrates a Wikimedia Commons media store and a large multimodal pre-trained model to bridge the gap between different modalities.
Outcome:	The proposed approach performs significantly better than pure textual cross-lingual phrase retrieval on a benchmarked dataset covering eight language pairs.

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Wenjie Zhou, Minghua Zhang, Yunfang Wu

Text Generation

Challenge:	Existing work suffers from mismatching between question type and answer . existing work fails to generate questions with type how while answer is personal name .
Approach:	They propose to automatically predict the question type based on the input answer and context.
Outcome:	The proposed model improves on both SQuAD and MARCO datasets and improves accuracy on the input answer and context.

MIND Your Reasoning: A Meta-Cognitive Intuitive-Reflective Network for Dual-Reasoning in Multimodal Stance Detection (2026.acl-long)

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Bingbing Wang, Zhengda Jin, Bin Liang, Wenjie Li, Jing Li, Ruifeng Xu, Min Zhang

Sentiment Analysis

Challenge:	Existing methods operate by learning to fuse modalities, leading to frequent misjudgments.
Approach:	They propose a paradigm shift from learning to fuse to *learning the reason's process' inspired by the dual-process theory of human cognition, MIND operationalizes a self-improving loop.
Outcome:	The proposed model significantly outperforms baseline models and exhibits strong generalization.

Measuring What Makes You Unique: Difference-Aware User Modeling for Enhancing LLM Personalization (2025.findings-acl)

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Yilun Qiu, Xiaoyan Zhao, Yang Zhang, Yimeng Bai, Wenjie Wang, Hong Cheng, Fuli Feng, Tat-Seng Chua

Challenge:	Extensive experiments on real-world datasets demonstrate that DPL significantly enhances LLM personalization.
Approach:	They propose a novel approach that emphasizes extracting inter-user differences to enhance LLM personalization.
Outcome:	The proposed approach extracts inter-user differences to enhance LLM personalization.

Interleaved Tool-Call Reasoning for Protein Function Understanding (2026.acl-long)

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Chuanliu Fan, Zicheng Ma, Huanran Meng, Aijia Zhang, Wenjie Du, Jun Zhang, Ziqiang Cao, Guohong Fu

Retrieval-Augmented Language Models are Mimetic Theorem Provers (2025.findings-emnlp)

Challenge:	Recent advances in large language models have highlighted the effectiveness of chain-of-thought reasoning in symbolic domains such as mathematics and programming.
Approach:	They propose a tool-augmented protein reasoning agent that unifies problem decomposition, tool invocation, and grounded answer generation.
Outcome:	The proposed protein function understanding agent outperforms text-only reasoning models with an average performance improvement of 103%.

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Wenjie Yang, Ruiyuan Huang, Jiaxing Guo, Zicheng Lyu, Tongshan Xu, Shengzhong Zhang, Lun Du, Da Zheng, Zengfeng Huang

Empowering Tabular Data Preparation with Language Models: Why and How? (2026.acl-long)

Challenge:	Large language models often fail to provide rigorous proof-based reasoning for research-level mathematics.
Approach:	They propose a simple yet effective RAG framework that augments retrieved proofs with queries and document contexts to improve retrieval performance.
Outcome:	The proposed framework improves retrieval performance by 34.19% . dual RAG can be used to prove research-level theorems in theoretical machine learning .

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Mengshi Chen, Yuxiang Sun, Tengchao Li, Jianwei Wang, Kai Wang, Xuemin Lin, Ying Zhang, Wenjie Zhang

VIBE: Topic-Driven Temporal Adaptation for Twitter Classification (2023.emnlp-main)

Challenge:	Tabular data preparation is a critical step in enhancing the usability of tabular data.
Approach:	They analyze how LMs can be combined with other components for different tabular data preparation tasks.
Outcome:	The proposed methods lack the ability to capture the relationships within tables and adapt to the tasks involved.

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Yuji Zhang, Jing Li, Wenjie Li

Information Retrieval

Challenge:	Language features are evolving in real-world social media, resulting in deteriorating performance of text classification.
Approach:	They propose a model that allows models to adapt to shifted data via latent topic evolution . they use two information bottleneck regularizers to distinguish past and future topics .
Outcome:	The proposed model outperforms state-of-the-art models on Twitter on three tasks with 3% of data.

OpenResearcher: Unleashing AI for Accelerated Scientific Research (2024.emnlp-demo)

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Yuxiang Zheng, Shichao Sun, Lin Qiu, Dongyu Ru, Cheng Jiayang, Xuefeng Li, Jifan Lin, Binjie Wang, Yun Luo, Renjie Pan, Yang Xu, Qingkai Min, Zizhao Zhang, Yiwen Wang, Wenjie Li, Pengfei Liu

Information Retrieval

Challenge:	Global scientific publications are growing annually by about 4%-5% (Pinedo et al., 2024).
Approach:	They introduce an AI-assisted platform that answers diverse questions from researchers using Retrieval-Augmented Generation (RAG) they develop various tools to understand queries, search from the scientific literature, filter retrieved information, provide accurate and comprehensive answers, and self-refine answers.
Outcome:	OpenResearcher is built on Retrieval-Augmented Generation (RAG) to integrate Large Language Models (LLMs) with up-to-date, domain-specific knowledge.

KBioXLM: A Knowledge-anchored Biomedical Multilingual Pretrained Language Model (2023.findings-emnlp)

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Lei Geng, Xu Yan, Ziqiang Cao, Juntao Li, Wenjie Li, Sujian Li, Xinjie Zhou, Yang Yang, Jun Zhang

Multilinguality

Challenge:	Existing models for multilingual biomedical training are monolingual, resulting in limited cross-lingual capability.
Approach:	They propose a model that transforms a multilingual biomedical corpus into a biomedically domain using a knowledge-anchored approach.
Outcome:	The proposed model outperforms monolingual and multilingual models in cross-lingual scenarios.

ExeCoder: Empowering Large Language Models with Executability Representation for Code Translation (2025.emnlp-main)

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Minghua He, Yue Chen, Fangkai Yang, Pu Zhao, Wenjie Yin, Yu Kang, Qingwei Lin, Saravan Rajmohan, Dongmei Zhang

HellaSwag-Pro: A Large-Scale Bilingual Benchmark for Evaluating the Robustness of LLMs in Commonsense Reasoning (2025.findings-acl)

Challenge:	Existing code translation models only learn the contextual semantics of code during pre-training, neglecting executability information closely related to the execution state of the code.
Approach:	They propose an LLM specifically designed for code translation called ExeCoder . it uses executability representations such as functional semantics and syntax structures to enhance LLMs' capabilities.
Outcome:	The proposed model outperforms existing open-source code translation models on two metrics.

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Xiaoyuan Li, Moxin Li, Rui Men, Yichang Zhang, Keqin Bao, Wenjie Wang, Fuli Feng, Dayiheng Liu, Junyang Lin

Robustness in NLP

Challenge:	Existing studies show that large language models are robust in commonsense reasoning . however, some variations in questions can lead to incorrect responses .
Approach:	They propose a large-scale bilingual benchmark consisting of 11,200 cases . they conduct extensive experiments on 41 representative LLMs .
Outcome:	The proposed benchmark systematically evaluates the robustness of large language models in commonsense reasoning.

MTR-Bench: A Comprehensive Benchmark for Multi-Turn Reasoning Evaluation (2026.acl-long)

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Xiaoyuan Li, Keqin Bao, Yubo Ma, Moxin Li, Wenjie Wang, Rui Men, Yichang Zhang, Fuli Feng, Dayiheng Liu

RiskLab: A Controlled Toolkit for Probing Emergent Risks in LLM-Based Multi-Agent Systems (2026.acl-demo)

Challenge:	Recent advances in Large Language Models (LLMs) have shown promising results in complex reasoning tasks.
Approach:	They propose to use a multi-turn reasoning evaluation framework to cover multi-turn interactions with the environments of large language models.
Outcome:	The proposed framework covers diverse reasoning capabilities, fine-grained difficulty granularity, and necessitates multi-turn interactions with the environments.

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Yu Jiang, Wenjie Wang, Yue Huang, Yanbo Wang, Zhenhong Zhou, Xiuying Chen, Yang Liu, Pin-Yu Chen, Wei Wang, Xiangliang Zhang

MMAPG: A Training-Free Framework for Multimodal Multi-hop Question Answering via Adaptive Planning Graphs (2025.emnlp-main)

Challenge:	Recent advances in large language model (LLM) agents have accelerated deployment of multi-agent systems for complex tasks.
Approach:	They propose an open-source toolkit for instantiating, probing, and measuring emergent risks in LLM-based multi-agent systems under controlled conditions.
Outcome:	The proposed toolkit is based on a structured topology–environment–protocol–agent–task quintuple enabling reproducible studies of how communication structure, coordination mechanisms, and incentives shape system-level risks.

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Yiheng Hu, Xiaoyang Wang, Qing Liu, Xiwei Xu, Qian Fu, Wenjie Zhang, Liming Zhu

Evaluating Mathematical Reasoning of Large Language Models: A Focus on Error Identification and Correction (2024.findings-acl)

Challenge:	Existing multimodal question answering models rely on sequential retrieval and reasoning, but this single-path paradigm makes them vulnerable to errors due to misleading intermediate steps.
Approach:	They propose a multimodal multi-hop question answering framework guided by an Adaptive Planning Graph . they propose modality-specific strategies that dynamically adapt to distinct data types .
Outcome:	The proposed framework outperforms existing models that rely on training.

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Xiaoyuan Li, Wenjie Wang, Moxin Li, Junrong Guo, Yang Zhang, Fuli Feng

KNN-SSD: Enabling Dynamic Self-Speculative Decoding via Nearest Neighbor Layer Set Optimization (2026.findings-eacl)

Challenge:	Existing evaluations focus on problem-solving from examiner perspective, overlooking a dual perspective of examiner regarding error identification and correction.
Approach:	They propose to use an annotated dataset to evaluate large language models from the examiner perspective and to use diverse prompts to evaluate eleven representative LLMs.
Outcome:	The proposed model outperforms all models while LLaMA-2-7B has comparable abilities to closed-source models GPT-3.5 and Gemini Pro.

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Mingbo Song, Heming Xia, Jun Zhang, Chak Tou Leong, Qiancheng Xu, Wenjie Li, Sujian Li

Challenge:	Large language models (LLMs) have proven highly capable in handling downstream tasks, but the token-by-token generation in autoregressive decoding results in quadratic computational complexity.
Approach:	They propose a method that proposes skipping certain layers to construct a draft model, which eliminates the need for additional parameters or training.
Outcome:	The proposed method achieves 1.31.6 speedup in LLM inference while being sensitive to domain shifts.

Text-like Encoding of Collaborative Information in Large Language Models for Recommendation (2024.acl-long)

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Yang Zhang, Keqin Bao, Ming Yan, Wenjie Wang, Fuli Feng, Xiangnan He

Self-Improvement Towards Pareto Optimality: Mitigating Preference Conflicts in Multi-Objective Alignment (2025.findings-acl)

Challenge:	Existing methods to adapt Large Language Models for Recommendation (LLMRec) do not represent collaborative information in a text-like format, which may not align optimally with LLMs.
Approach:	They propose a novel LLMRec method that integrates collaborative information through text-like encoding.
Outcome:	Extensive experiments show that BinLLM integrates collaborative information better with LLMs.

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Moxin Li, Yuantao Zhang, Wenjie Wang, Wentao Shi, Zhuo Liu, Fuli Feng, Tat-Seng Chua

CI-Work: Benchmarking Contextual Integrity in Enterprise LLM Agents (2026.acl-industry)

Challenge:	Existing approaches to optimize large language models with human preferences suffer from preference conflicts in the data.
Approach:	They propose to construct Pareto-optimal responses to resolve preference conflicts by using a self-improving DPO framework that enables LLMs to self-generate and select Paret-optimized responses.
Outcome:	The proposed framework achieves superior Pareto Front performance over baselines on two datasets.

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Wenjie Fu, Xiaoting Qin, Jue Zhang, Qingwei Lin, Lukas Wutschitz, Robert Sim, Saravan Rajmohan, Dongmei Zhang

Customizing In-context Learning for Dynamic Interest Adaption in LLM-based Recommendation (2025.findings-acl)

Challenge:	Enterprise LLM agents can dramatically improve workplace productivity, but their core capability, retrieving and using internal context to act on a user’s behalf, also creates new risks for sensitive information leakage.
Approach:	They propose a Contextual Integrity-grounded benchmark that simulates enterprise workflows across five information-flow directions and evaluates whether agents can convey essential content while withholding sensitive context in dense retrieval settings.
Outcome:	The proposed model demonstrates that privacy failures are prevalent in enterprise workflows and that higher task utility correlates with increased privacy violations.

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Keqin Bao, Ming Yan, Yang Zhang, Jizhi Zhang, Wenjie Wang, Fuli Feng, Xiangnan He

Low-Resource NLP

Challenge:	Existing Large Language Model (LLM)-based recommender systems face challenges to adapt to dynamic user interests without any model-level updates.
Approach:	They propose a framework that establishes recommendation-oriented in-context learning by structuring recent user interactions and current inputs into ICL formats.
Outcome:	The proposed model adapts to dynamic user interests without model updates without any model updates and is available online at https://anonymous.4open.science/r/RecICL-8003.

Robust Prompt Optimization for Large Language Models Against Distribution Shifts (2023.emnlp-main)

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Moxin Li, Wenjie Wang, Fuli Feng, Yixin Cao, Jizhi Zhang, Tat-Seng Chua

A Survey on Open Information Extraction from Rule-based Model to Large Language Model (2024.findings-emnlp)

Challenge:	Existing research has explored automatic prompt optimization methods to eliminate manual effort in identifying effective prompts for a given task.
Approach:	They propose a framework for prompt optimization that can be generalized to an unlabeled target group.
Outcome:	The proposed framework improves on target group and source group while generalizing to unlabeled target group.

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Liu Pai, Wenyang Gao, Wenjie Dong, Lin Ai, Ziwei Gong, Songfang Huang, Li Zongsheng, Ehsan Hoque, Julia Hirschberg, Yue Zhang

Counterfactual Active Learning for Out-of-Distribution Generalization (2023.acl-long)

Challenge:	Open Information Extraction (OpenIE) is a key NLP task aimed at extracting structured information from unstructured text sources.
Approach:	They propose to categorize OpenIE into rule-based, neural, and pre-trained large language models and discuss each within a chronological framework.
Outcome:	The paper categorizes OpenIE approaches into rule-based, neural, and pre-trained large language models, discussing each within a chronological framework.

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Xun Deng, Wenjie Wang, Fuli Feng, Hanwang Zhang, Xiangnan He, Yong Liao

Mask the Correct Tokens: An Embarrassingly Simple Approach for Error Correction (2022.emnlp-main)

Challenge:	Existing studies on active learning methods focus on the out-of-distribution generalization of out- of-distortion samples.
Approach:	They propose a counterfactual active learning approach that empowers active learning with counterfact thinking to bridge the seen samples with unseen cases.
Outcome:	The proposed approach outperforms existing active learning methods on public datasets with comparable IID performance.

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Kai Shen, Yichong Leng, Xu Tan, Siliang Tang, Yuan Zhang, Wenjie Liu, Edward Lin

Unpaired Sentiment-to-Sentiment Translation: A Cycled Reinforcement Learning Approach (P18-1)

Challenge:	Text error correction methods usually use the source (incorrect) sentence as encoder input and generate the target (correct) sentences through the decoder.
Approach:	They propose a method to correct errors in text sequences by randomly masking out the correct tokens in the source sentence.
Outcome:	The proposed method improves accuracy on Mandarin and English datasets with autoregressive and non-autoregressive generation models.

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Jingjing Xu, Xu Sun, Qi Zeng, Xiaodong Zhang, Xuancheng Ren, Houfeng Wang, Wenjie Li

Machine Translation

Challenge:	Existing studies for sentiment-to-sentiment "translation" only change the underlying sentiment and fail to keep the semantic content.
Approach:	They propose a cycled reinforcement learning method that combines neutralization module and emotionalization module.
Outcome:	The proposed method outperforms state-of-the-art systems on Yelp and Amazon review datasets.

Agent-based Substructure Counting under Local Differential Privacy (2026.acl-long)

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Yuting Zhang, Kai Wang, Wei Ni, Ying Zhang, Wenjie Zhang

HydraRAG: Structured Cross-Source Enhanced Large Language Model Reasoning (2025.emnlp-main)

Challenge:	Recent studies have demonstrated the ability of Large Language Models (LLMs) to process graph problems.
Approach:	They propose to decompose substructure counting into node-level tasks distributed among node agents and embed the knowledge of distributed algorithms and DP frameworks in the curator agent and privacy controller.
Outcome:	Extensive experiments on 6 real-world datasets validate the effectiveness of the proposed framework for substructure counting tasks under edge local differential privacy (LDP).

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Xingyu Tan, Xiaoyang Wang, Qing Liu, Xiwei Xu, Xin Yuan, Liming Zhu, Wenjie Zhang

Query Routing for Homogeneous Tools: An Instantiation in the RAG Scenario (2024.findings-emnlp)

Challenge:	Current RAG system retrieves evidence from knowledge graphs and text documents but has limitations in multi-hop reasoning, multi-entity questions, and source verification.
Approach:	They propose a training-free framework that unifies graph topology, document semantics, and source reliability to support deep, faithful reasoning in large language models.
Outcome:	The proposed framework outperforms the current hybrid model-based model-driven system by 20.3% and 30.1% on seven benchmark datasets.

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Feiteng Mu, Yong Jiang, Liwen Zhang, Liuchu Liuchu, Wenjie Li, Pengjun Xie, Fei Huang

Challenge:	Existing tool learning methods focus on selecting the most effective tool from a wide array of options, often overlooking cost-effectiveness.
Approach:	They propose to predict query performance and cost required to accomplish a given task . they then assign queries to the optimal tools in a cost-effective manner .
Outcome:	The proposed method achieves higher performance at lower cost compared to baseline approaches.

GOVERN: Gradient Orientation Vote Ensemble for Multi-Teacher Reinforced Distillation (2024.emnlp-industry)

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Wenjie Zhou, Zhenxin Ding, Xiaodong Zhang, Haibo Shi, Junfeng Wang, Dawei Yin

Distillation Enhanced Generative Retrieval (2024.findings-acl)

Challenge:	Pre-trained language models have achieved remarkable performance in OpenQA, but for practical deployment, knowledge distillation is crucial to maintain high performance while operating under computational constraints.
Approach:	They propose an algorithm to perform unsupervised knowledge distillation without the guidance of labels to achieve 99.5% of performance.
Outcome:	The proposed algorithm achieves 99.5% of performance in a commercial question-answering system.

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Yongqi Li, Zhen Zhang, Wenjie Wang, Liqiang Nie, Wenjie Li, Tat-Seng Chua

Towards Text-Image Interleaved Retrieval (2025.acl-long)

Challenge:	Generative retrieval is a promising new paradigm in text retrieval that generates identifier strings of relevant passages as the retrieval target.
Approach:	They propose a framework that leverages generative language models to enhance generative retrieval by distillation.
Outcome:	The proposed framework achieves state-of-the-art performance among the generative retrieval methods.

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Xin Zhang, Ziqi Dai, Yongqi Li, Yanzhao Zhang, Dingkun Long, Pengjun Xie, Meishan Zhang, Jun Yu, Wenjie Li, Min Zhang

Multi-Task Learning with Language Modeling for Question Generation (D19-1)

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.

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Wenjie Zhou, Minghua Zhang, Yunfang Wu