Papers by Jiajun Wu
Towards Multi-label Unknown Intent Detection (2022.coling-1)
Copied to clipboard
| Challenge: | Existing methods for multi-class unknown intent detection assume that each utterance has only one intent, which is not true because utterrances often contain multiple intents. |
| Approach: | They propose a task to detect whether an utterance contains the unknown intent by recognizing whether all intents contained in the utterant are known. |
| Outcome: | The proposed method significantly reduces the FPR95 on the MultiWOZ 2.3 dataset by 12.25% compared to the best baseline. |
Addressing Troublesome Words in Neural Machine Translation (D18-1)
Copied to clipboard
| Challenge: | Neural machine translation (NMT) has weaknesses in handling lowfrequency and ambiguous words, which we refer to as troublesome words. |
| Approach: | They propose to use contextual memory to memorize which target words should be produced in which situations to translate troublesome words. |
| Outcome: | The proposed method outperforms baseline models on Chinese-to-English and English-to German translation tasks. |
Taming System Complexity: Demystifying Software Engineering Agents in Diagnosing Linux Kernel Faults (2026.acl-long)
Copied to clipboard
| Challenge: | Existing LLM agents struggle with identifying bugs in the Linux kernel . bugs can affect billions of users, affecting the Linux Foundation's research on the topic . |
| Approach: | They propose a LinuxFLBench benchmark to measure the accuracy of LLM agents on the Linux kernel. |
| Outcome: | The proposed framework improves FL accuracy with minimal costs. |
LoopCoder: Scaling Code Intelligence via Looped Language Models (2026.findings-acl)
Copied to clipboard
Jian Yang, Wei Zhang, Shuyue Guo, Yizhi LI, Linzheng Chai, Zhengmao Ye, Shukai Liu, Yuyang Song, Jiajun Wu, Che Liu, Tianyu Zheng, Siwei Wu, Leo L, Xudong Ma, Chuan Hao, Ran Tao, Yan Xing, Jianzhou Wang, Mingjie Tang, Aishan Liu, Zhoujun Li, Xianglong Liu, Weifeng Lv, Bryan Dai
| Challenge: | Large language models have mastered syntax-level code generation, but complex algorithmic reasoning remains a challenge. |
| Approach: | They propose a recurrent inductive bias that aligns with the recursive nature of programming logic. |
| Outcome: | The proposed model achieves comparable performance to standard dense models with more parameters. |
RealChart2Code: Bridging the Gap in Real-World Chart-to-Code Generation via Multi-Task Evaluation (2026.acl-long)
Copied to clipboard
Jiajun Zhang, Yuying Li, Zhixun Li, Xingyu Guo, Jingzhuo Wu, Leqi Zheng, Yiran Yang, Jianke Zhang, Qingbin Li, Shannan Yan, Changguo Jia, Junfei Wu, Zilei Wang, Qiang Liu, Liang Wang
| Challenge: | Vision-Language Models (VLMs) have demonstrated impressive capabilities in code generation across various domains, but their ability to replicate complex, multi-panel visualizations remains largely unassessed. |
| Approach: | They propose a large-scale benchmark to evaluate chart generation from large- scale raw data and assess iterative code refinement in a multi-turn conversational setting. |
| Outcome: | The new benchmark evaluates 14 leading VLMs on real-world data and shows they struggle with complex plot structures and authentic data. |
A Data-Efficient Path to Multilingual LLMs: Language Expansion via Post-training PARAM𝛥 Integration into Upcycled MoE (2026.acl-long)
Copied to clipboard
Hao Zhou, Tianhao Li, Zhijun Wang, Shuaijie She, Linjuan Wu, Hao-Ran Wei, Baosong Yang, Jiajun Chen, Shujian Huang
| Challenge: | Large Language Models (LLMs) are expensive and require extensive Continued Pre-Training and data-intensive alignment to expand. |
| Approach: | They propose a method which upcycles a dense model into a Mixture-of-Experts architecture, allocating different experts to different languages. |
| Outcome: | Experiments show that the proposed model upcycles a dense model into a Mixture-of-Experts(MoE) architecture, allocating different experts to different languages. |
Learning from Adjective-Noun Pairs: A Knowledge-enhanced Framework for Target-Oriented Multimodal Sentiment Classification (2022.coling-1)
Copied to clipboard
| Challenge: | Existing methods to determine sentiment polarity of opinion target are inconsistent and lack visual attention. |
| Approach: | They propose a framework which can exploit adjective-noun pairs extracted from images to improve visual attention and sentiment prediction capability of the TMSC task. |
| Outcome: | The proposed framework outperforms state-of-the-art on two public datasets. |
TriSPrompt: A Hierarchical Soft Prompt Model for Multimodal Rumor Detection with Incomplete Modalities (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Existing multimodal rumor detection methods focus on learning joint modality representations from complete multimodal training data, rendering them ineffective in addressing the common occurrence of missing modalities in real-world scenarios. |
| Approach: | They propose a hierarchical soft prompt model TriSPrompt which integrates three types of prompts to effectively detect rumors in incomplete multimodal data. |
| Outcome: | The proposed model achieves an accuracy gain of over 13% compared to state-of-the-art models. |
LADM: Long-context Training Data Selection with Attention-based Dependency Measurement for LLMs (2025.acl-long)
Copied to clipboard
| Challenge: | Long-context modeling has drawn more attention in the area of Large Language Models (LLMs). |
| Approach: | They propose a Long-context data selection framework with Attention-based Dependency Measurement which can efficiently identify high-quality long-contrast data from a large-scale, multi-domain pre-training corpus. |
| Outcome: | The proposed framework significantly boosts the performance of LLMs on multiple long-context tasks with only 1B tokens for continual training. |
EvoHyper: Evolving Hypergraph Topologies for Unified Collaboration in Multi-Agent Communication (2026.findings-acl)
Copied to clipboard
Heng Zhang, Yihao Zhong, Lubin Gan, Zhihe Chen, Jiajun Wu, Yuling Shi, Xiaodong Gu, Hao Zhang, Haochen You, Jin Huang
| Challenge: | Existing methods for multi-agent collaboration use a fixed communication graph and manage collaboration structure and shared memory in separate modules. |
| Approach: | They propose a framework that uses an evolving hypergraph topology for multi-agent collaboration. |
| Outcome: | The proposed framework achieves 3.2% to 7.8% accuracy gains over state-of-the-art methods and efficient, reducing token consumption by up to 23.5%. |
OMIBench: Benchmarking Olympiad-Level Multi-Image Reasoning in Large Vision-Language Models (2026.acl-long)
Copied to clipboard
Qiguang Chen, Chengyu Luan, Jiajun Wu, Qiming Yu, Yi Yang, Yizhuo Li, Jingqi Tong, Xiachong Feng, Libo Qin, Wanxiang Che
| Challenge: | Existing multimodal reasoning benchmarks for large vision-language models emphasize single-image analysis and fail to exploit contextual information across multiple images. |
| Approach: | They propose a benchmark to evaluate Olympiad-level reasoning when evidence is distributed over multiple images. |
| Outcome: | The proposed model outperforms existing models on bi-image Olympiads and Gemini-3-Pro on multimodal Olympiad-level reasoning tasks. |
OpenS2S: Advancing Fully Open-Source End-to-End Empathetic Large Speech Language Model (2025.emnlp-demos)
Copied to clipboard
Chen Wang, Tianyu Peng, Wen Yang, YiNan Bai, Guangfu Wang, Jun Lin, Lanpeng Jia, Lingxiang Wu, Jinqiao Wang, Chengqing Zong, Jiajun Zhang
| Challenge: | Empathetic speech models are increasingly closed off, leaving details about the architecture, data and development opaque to researchers. |
| Approach: | They propose an open-source empathetic speech-to-text model with a streaming interleaved decoding architecture and a data pipeline to enable end-to end training. |
| Outcome: | The proposed model is open-source and transparent, with no data or data required to build it. |
Learning Confidence for Transformer-based Neural Machine Translation (2022.acl-long)
Copied to clipboard
| Challenge: | A well-calibrated confidence estimate is not sufficient for neural machine translation (NMT) where probabilities from softmax distribution fail to describe when the model is probably mistaken. |
| Approach: | They propose an unsupervised confidence estimate learning jointly with the training of a neural machine translation model to quantify confidence. |
| Outcome: | The proposed model outperforms standard label smoothing and can predict failures in two real-world scenarios. |
Language-Mediated, Object-Centric Representation Learning (2021.findings-acl)
Copied to clipboard
| Challenge: | Recent work has studied the problem of unsupervised object representation learning, though without language. |
| Approach: | They propose language-mediated, Objectcentric Representation Learning (LORL) a paradigm for learning disentangled, objectcentric scene representations from vision and language. |
| Outcome: | The proposed paradigm improves performance of unsupervised object discovery algorithms on two datasets using language. |
V-GameGym: Visual Game Generation for Code Large Language Models (2026.findings-acl)
Copied to clipboard
Wei Zhang, Jian Yang, Renshuai Tao, Linzheng Chai, Shuyue Guo, Jiajun Wu, Xiaoming Chen, Ganqu Cui, Ning Ding, Xander Xu, HU Wei, Bowen Zhou
| Challenge: | Existing code-related benchmarks focus on single modality rather than visual game development. |
| Approach: | They propose a multimodal benchmark for evaluating code large language models in visual game generation that integrates a clustering-based curation methodology and a pipeline for visual code synthesis. |
| Outcome: | The proposed framework assesses code generation and visual game generation using a sandbox environment. |
BLSP-Emo: Towards Empathetic Large Speech-Language Models (2024.emnlp-main)
Copied to clipboard
| Challenge: | BLSP-Emo model understands both semantics and emotions in speech and generates empathetic responses. |
| Approach: | They propose a language-speech pretraining with emotion support that utilizes existing speech and emotion recognition datasets to create an end-to-end speech-language model. |
| Outcome: | The proposed model can understand both semantics and emotions in speech and generate empathetic responses. |
From Behavioral Performance to Internal Competence: Interpreting Vision-Language Models with VLM-Lens (2025.emnlp-demos)
Copied to clipboard
Hala Sheta, Eric Haoran Huang, Shuyu Wu, Ilia Alenabi, Jiajun Hong, Ryker Lin, Ruoxi Ning, Daniel Wei, Jialin Yang, Jiawei Zhou, Ziqiao Ma, Freda Shi
| Challenge: | Existing vision-language models are based on exactmatch based accuracy and its derivations to evaluate performance. |
| Approach: | They propose a toolkit that supports systematic benchmarking, analysis, and interpretation of vision-language models by extracting intermediate outputs from any layer during the forward pass of open-source VLMs. |
| Outcome: | The proposed toolkit supports 16 state-of-the-art base VLMs and their over 30 variants and is extensible to accommodate new models without changing the core logic. |
Implicit Cross-Lingual Rewarding for Efficient Multilingual Preference Alignment (2025.findings-acl)
Copied to clipboard
| Challenge: | Existing approaches to align English LLMs with human preferences rely on expensive human annotations or advanced multilingual preference alignment models. |
| Approach: | They propose a method that captures learned preferences from English models by implicit rewards . they annotate preference relations in cross-lingual instruction-following pairs using English . |
| Outcome: | The proposed approach captures learned preferences from well-aligned English models by implicit rewards and transfers them to other languages through iterative training. |
Beyond the Individual: Virtualizing Multi-Disciplinary Reasoning for Clinical Intake via Collaborative Agents (2026.findings-acl)
Copied to clipboard
Huangwei Chen, Wu Li, Junhao Jia, Yining Chen, Xiaotao Pang, Ya-Long Chen, Li Gonghui, Haishuai Wang, Jiajun Bu, Lei Wu
| Challenge: | Initial outpatient consultations are costly and difficult to scale to real-time intake. |
| Approach: | They propose a synchronous virtual MDT framework that formalizes the consultation state using a structured SOAP representation, separating evidence collection from diagnostic reasoning to improve traceability and bias control. |
| Outcome: | The proposed framework outperforms state-of-the-art models on ClinicalBench and a real-world RAPID-IPN dataset in documentation quality and consultation capability. |
Target-oriented Opinion Words Extraction with Target-fused Neural Sequence Labeling (N19-1)
Copied to clipboard
| Challenge: | Opinion target extraction and opinion words extraction are two fundamental subtasks in Aspect Based Sentiment Analysis (ABSA). |
| Approach: | They propose a new subtask for Aspect Based Sentiment Analysis to extract opinion words as pairs from a given opinion target. |
| Outcome: | The proposed model outperforms existing methods significantly on several popular ABSA benchmarks. |
Hit the Sweet Spot! Span-Level Ensemble for Large Language Models (2025.coling-main)
Copied to clipboard
| Challenge: | a recent study focused on sample-level and token-level ensembles, which hinder dynamic correction and enhancement of outputs during the generation process. |
| Approach: | They propose a span-level ensemble method that balances real-time adjustments and accurate ensemble decisions. |
| Outcome: | The proposed method improves performance across language generation tasks significantly. |
Learning Representation Mapping for Relation Detection in Knowledge Base Question Answering (P19-1)
Copied to clipboard
| Challenge: | Existing approaches to detect relation detection only get high accuracy for questions whose relations have been seen in training data. |
| Approach: | They propose a method to learn representation mapping for both seen and unseen relations based on previously learned relation embedding. |
| Outcome: | The proposed method improves the performance of unseen relations while keeping the performance comparable to the state-of-the-art. |
COIG-CQIA: Quality is All You Need for Chinese Instruction Fine-tuning (2025.findings-naacl)
Copied to clipboard
Yuelin Bai, Xeron Du, Yiming Liang, Leo Jin, Junting Zhou, Ziqiang Liu, Feiteng Fang, Mingshan Chang, Tianyu Zheng, Xincheng Zhang, Nuo Ma, Zekun Moore Wang, Ruibin Yuan, Haihong Wu, Hongquan Lin, Wenhao Huang, Jiajun Zhang, Chenghua Lin, Jie Fu, Min Yang, Shiwen Ni, Ge Zhang
| Challenge: | Existing datasets for Chinese instruction tuning are not well-aligned with Chinese users’ interaction patterns. |
| Approach: | They propose to use Chinese instruction tuning datasets to improve instruction fine-tuning for Chinese users. |
| Outcome: | The proposed dataset shows that Chinese models achieve competitive performance in diverse benchmarks. |
UCoder: Unsupervised Code Generation by Internal Probing of Large Language Models (2026.findings-acl)
Copied to clipboard
Jiajun Wu, Jian Yang, Wei Zhang, Linzheng Chai, Yuchi Ma, Ensheng Shi, Yuqing Ma, Zhoujun Li, Xianglong Liu
| Challenge: | Large language models (LLMs) have demonstrated remarkable capabilities in code generation tasks, but their effectiveness relies on supervised training with extensive labeled data and computational resources. |
| Approach: | They propose an unsupervised method that leverages Internal Probing of Large language models for Code generation without any external corpus, even unlabeled code snippets. |
| Outcome: | The proposed method can achieve competitive performance compared to supervised approaches while reducing the dependency on labeled data and computational resources. |
LAGCL4Rec: When LLMs Activate Interactions Potential in Graph Contrastive Learning for Recommendation (2025.findings-emnlp)
Copied to clipboard
Leqi Zheng, Chaokun Wang, Canzhi Chen, Jiajun Zhang, Cheng Wu, Zixin Song, Shannan Yan, Ziyang Liu, Hongwei Li
| Challenge: | Traditional contrastive learning methods treat negative feedback as equally hard or easy, ignoring informative semantic difficulty during training. |
| Approach: | They propose a framework leveraging Large Language Models to Activate interactions in Graph Contrastive Learning for Recommendation. |
| Outcome: | The proposed framework outperforms state-of-the-art benchmarks on multiple benchmarks. |
Golden Touchstone: A Comprehensive Bilingual Benchmark for Evaluating Financial Large Language Models (2025.findings-emnlp)
Copied to clipboard
Xiaojun Wu, Junxi Liu, Huan-Yi Su, Zhouchi Lin, Yiyan Qi, Chengjin Xu, Jiajun Su, Jiajie Zhong, Fuwei Wang, Saizhuo Wang, Fengrui Hua, Jia Li, Jian Guo
| Challenge: | Existing financial benchmarks suffer from limited language and task coverage, low-quality datasets, and inadequate adaptability for LLM evaluation. |
| Approach: | They propose a bilingual benchmark for financial LLMs that assesses models’ language understanding and generation capabilities. |
| Outcome: | The proposed bilingual benchmark assesses models’ language understanding and generation capabilities. |
Attention Calibration for Transformer in Neural Machine Translation (2021.acl-long)
Copied to clipboard
| Challenge: | Attention mechanisms have been ubiquitous in neural machine translation (NMT) however, many studies doubt whether highlyattended inputs have a large impact on the model outputs. |
| Approach: | They propose to introduce a mask perturbation model that automatically evaluates each input’s contribution to the model outputs. |
| Outcome: | The proposed model is more uniform at lower layers while more concentrated on the specific inputs at higher layers. |
SCOUT: Selective Coupling via Optimal Unbalanced Transport for Interpretable Text Classification (2026.acl-long)
Copied to clipboard
| Challenge: | Standard interpretable models often rely on scalar similarities that obscure the true evidentiary basis of a prediction. |
| Approach: | They propose a new paradigm that grounds prototype reasoning in the selective correspondence of discriminative fragments. |
| Outcome: | The proposed model outperforms rationale extraction and post-hoc attribution methods on seven benchmarks. |
Look Again, Think Slowly: Enhancing Visual Reflection in Vision-Language Models (2025.emnlp-main)
Copied to clipboard
| Challenge: | Recent advances in text-only "slow thinking" reasoning have prompted efforts to transfer this capability to vision-language models (VLMs). |
| Approach: | They propose a VRM Reflection-V which enhances visual reflection based on reasoning data for cold-start and reward design for reinforcement learning. |
| Outcome: | The proposed model improves visual reflection for cold-start and reward design for reinforcement learning (RL) it maintains a stronger and more consistent reliance on visual information during visual reasoning, indicating effective enhancement in visual reflection capabilities. |