Papers by Xin Jing
Knowledge-augmented Self-training of A Question Rewriter for Conversational Knowledge Base Question Answering (2022.findings-emnlp)
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| Challenge: | Recent rise of conversational applications has promoted the development of conversation KBQA (ConvKBQA). |
| Approach: | They propose a framework to produce a full-fledged rewritten question based on conversation history and then reason the answer by existing single-turn KBQA models. |
| Outcome: | The proposed framework produces a full-fledged rewritten question based on the conversation history and reasoned the answer by existing single-turn KBQA models. |
Enhancing Chain-of-Thought Reasoning via Neuron Activation Differential Analysis (2025.emnlp-main)
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| Challenge: | Existing studies focus on optimizing external components of CoT, but lack internal explanations for the quality of the model's outputs. |
| Approach: | They propose an efficient method to identify reasoning-critical neurons by analyzing their activation patterns under reasoning chains of varying quality. |
| Outcome: | The proposed method shows that neurons in the feed-forward layers are critical in the generation of high-quality reasoning chains. |
Muffin or Chihuahua? Challenging Multimodal Large Language Models with Multipanel VQA (2024.acl-long)
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Yue Fan, Jing Gu, Kaiwen Zhou, Qianqi Yan, Shan Jiang, Ching-Chen Kuo, Yang Zhao, Xinze Guan, Xin Wang
| Challenge: | Multipanel images are a common form of visual representations, and humans can achieve approximately 99% accuracy on these questions. |
| Approach: | They propose a benchmark that tests multipanel visual reasoning models with 6,600 triplets of questions, answers, and multipanel images. |
| Outcome: | The proposed benchmark features 6,600 triplets of questions, answers, and multipanel images that challenge state-of-the-art Multimodal Large Language Models (MLLMs) human users can attain approximately 99% accuracy on these questions, compared with previous benchmarks. |
HyperCL: A Contrastive Learning Framework for Hyper-Relational Knowledge Graph Embedding with Hierarchical Ontology (2024.findings-acl)
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| Challenge: | Existing studies neglect the ontology of knowledge Graph (KG) embeddings and suffer from the dominance issue of facts over ontologies. |
| Approach: | They propose a framework for hyper-relational KG embeddings that captures the hierarchical ontology and a concept-aware contrastive loss to alleviate the dominance issue. |
| Outcome: | The proposed framework improves on three real-world datasets and shows that it can integrate with other embedding methods and improve link prediction performance. |
Reasoning-Enhanced Domain-Adaptive Pretraining of Multimodal Large Language Models for Short Video Content Governance (2025.emnlp-industry)
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Zixuan Wang, Yu Sun, Hongwei Wang, Baoyu Jing, Xiang Shen, Xin Dong, Zhuolin Hao, Hongyu Xiong, Yang Song
| Challenge: | Existing approaches to identifying inappropriate content require extensive human-labeled data and lack cross-issue generalization. |
| Approach: | They propose a reasoning-enhanced multimodal large language model (MLLM) pretraining paradigm for unified inappropriate content detection. |
| Outcome: | The proposed model improves the MLLM's performance in both zero-shot and supervised fine-tuning settings and shows strong generalization capabilities to emergent, previously unseen issues. |
MMATH: A Multilingual Benchmark for Mathematical Reasoning (2025.findings-emnlp)
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| Challenge: | a benchmark for multilingual complex reasoning spans 374 high-quality math problems across 10 typologically diverse languages. |
| Approach: | They propose a benchmark for multilingual complex reasoning across 10 languages . they show reasoning in English and answering in target languages can enhance performance . |
| Outcome: | The proposed benchmark demonstrates that models with high-quality reasoning can perform in multiple languages. |
ODE Transformer: An Ordinary Differential Equation-Inspired Model for Sequence Generation (2022.acl-long)
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Bei Li, Quan Du, Tao Zhou, Yi Jing, Shuhan Zhou, Xin Zeng, Tong Xiao, JingBo Zhu, Xuebo Liu, Min Zhang
| Challenge: | Residual networks are an Euler discretization of solutions to Ordinary Differential Equations (ODE). |
| Approach: | They propose a residual block of layers in Transformer that can be described as a higher-order solution to ODE. |
| Outcome: | The proposed architecture can gain large improvements over strong baselines at a slight cost in inference efficiency. |
Subtopic-driven Multi-Document Summarization (D19-1)
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| Challenge: | Experimental results show that the proposed model outperforms state-of-the-art methods on benchmark datasets. |
| Approach: | They propose a multi-document summarization model that assumes a set of documents to be summarized is on the same topic. |
| Outcome: | The proposed model outperforms state-of-the-art methods on benchmark datasets. |
Reinforced Informativeness Optimization for Long-Form Retrieval-Augmented Generation (2026.acl-long)
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| Challenge: | Existing reinforcement learning systems lack verifiable reward mechanisms for long-form question answering . current systems lack reliable long-term answers due to lack of factual content . |
| Approach: | They propose a framework for reinforced verifiable informativeness optimization . it defines informativeness as measurable and externally verifier objective for RL . |
| Outcome: | Experiments show that RioRAG achieves higher factual recall and faithfulness . the proposed framework is based on a framework that uses nugget-centric verification with cross-source checks . |
AMR-Evol: Adaptive Modular Response Evolution Elicits Better Knowledge Distillation for Large Language Models in Code Generation (2024.emnlp-main)
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| Challenge: | proprietary large language models (LLMs) have demonstrated impressive code generation performance. |
| Approach: | They propose an adaptive module-based model that refines the direct response distillation process by modular decomposition and adaptive response evolution. |
| Outcome: | The proposed framework outperforms baseline model and code generation methods on three popular benchmarks. |
Hyperbolic Capsule Networks for Multi-Label Classification (2020.acl-main)
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| Challenge: | Existing methods for classification of labels are limited by feature aggregation and encoding. |
| Approach: | They propose to use hyperbolic capsule networks to capture fine-grained label information . they also propose a new routing method to adaptively adjust capsule number during routing . |
| Outcome: | The proposed method significantly improves the performance of multi-label classification on tail labels. |
NeuralClassifier: An Open-source Neural Hierarchical Multi-label Text Classification Toolkit (P19-3)
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| Challenge: | NeuralClassifier is a toolkit for hierarchical multi-label text classification. |
| Approach: | They propose a toolkit for neural hierarchical multi-label text classification . they use a variety of text encoders to implement the model . |
| Outcome: | The proposed model achieves comparable performance with reported results in the literature. |
MEIT: Multimodal Electrocardiogram Instruction Tuning on Large Language Models for Report Generation (2025.findings-acl)
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Zhongwei Wan, Che Liu, Xin Wang, Chaofan Tao, Hui Shen, Jing Xiong, Rossella Arcucci, Huaxiu Yao, Mi Zhang
| Challenge: | Recent studies have focused on classifying cardiac conditions using ECG data but have overlooked ECG report generation, which is time-consuming and requires clinical expertise. |
| Approach: | They propose a Multimodal ECG Instruction Tuning framework that extends the capability of large language models (LLMs) for the task. |
| Outcome: | The proposed framework outperforms open-source LLMs and LLM backbones across two large-scale ECG datasets. |
Continual Pre-training of Language Models for Math Problem Understanding with Syntax-Aware Memory Network (2022.acl-long)
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| Challenge: | a fundamental challenge in modeling math problems is how to fuse semantics of textual description and formulas. |
| Approach: | They propose a method to continually pre-train language models for improving understanding of math problems with syntax-aware memory networks. |
| Outcome: | The proposed approach outperforms competitive baselines on four math tasks. |
REAR: A Relevance-Aware Retrieval-Augmented Framework for Open-Domain Question Answering (2024.emnlp-main)
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| Challenge: | Existing methods to extend knowledge scope of large language models (LLMs) lack internal parametric knowledge, resulting in misusing external knowledge. |
| Approach: | They propose a retrieval-augmented approach that provides LLMs with potentially relevant documents through a module. |
| Outcome: | The proposed approach outperforms existing methods on four open-domain QA tasks. |
CodeAttack: Revealing Safety Generalization Challenges of Large Language Models via Code Completion (2024.findings-acl)
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| Challenge: | Large Language Models exhibit remarkable generative capabilities but can be misused for harmful purposes. |
| Approach: | They propose a framework that transforms natural language inputs into code inputs. |
| Outcome: | The proposed framework bypasses the safety guardrails of all models more than 80% of the time. |
HyperFM: Fact-Centric Multimodal Fusion for Link Prediction over Hyper-Relational Knowledge Graphs (2025.acl-long)
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| Challenge: | Existing link prediction techniques focus on learning the complex relationships between entities and relations while ignoring the multimodal information. |
| Approach: | They propose a fact-centric fusion technique that captures complex interactions between different data modalities while accommodating the hyper-relational structure of the KG in a facts-centric manner. |
| Outcome: | The proposed technique improves on two real-world KG datasets by 6.0-6.8% over baselines. |
LCQMC:A Large-scale Chinese Question Matching Corpus (C18-1)
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| Challenge: | Existing methods for question answering system lack large-scale question matching corpora . lack of large-sized question matching results in problem solving . |
| Approach: | They propose a large-scale Chinese question matching corpus which is released to the public . they use a search engine to collect large-sized question pairs related to high-frequency words . |
| Outcome: | The proposed corpus is more general than paraphrase corpus as it focuses on intent matching rather than paraphrasing. |
MIND: Multimodal Shopping Intention Distillation from Large Vision-language Models for E-commerce Purchase Understanding (2024.emnlp-main)
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Baixuan Xu, Weiqi Wang, Haochen Shi, Wenxuan Ding, Huihao Jing, Tianqing Fang, Jiaxin Bai, Xin Liu, Changlong Yu, Zheng Li, Chen Luo, Qingyu Yin, Bing Yin, Long Chen, Yangqiu Song
| Challenge: | Existing methods for acquiring large-scale intentions generate product-centric intentions without product images and incur high costs for scalability. |
| Approach: | They propose a multimodal framework that allows Large Vision-Language Models to infer purchase intentions from multimodal product metadata and prioritize human-centric ones. |
| Outcome: | The proposed framework shows that it is robust to different prompts and superior to previous methods. |
A Thorough Examination on Zero-shot Dense Retrieval (2023.findings-emnlp)
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Ruiyang Ren, Yingqi Qu, Jing Liu, Xin Zhao, Qifei Wu, Yuchen Ding, Hua Wu, Haifeng Wang, Ji-Rong Wen
| Challenge: | Recent advances in dense retrieval (DR) models have been shown to be not as competitive as traditional sparse retrieval models in a zero-shot retrieval setting. |
| Approach: | They propose to examine the zero-shot capability of DR models by analyzing key factors related to source training set and potential bias from target dataset. |
| Outcome: | The proposed model is not as competitive as sparse retrieval models in a zero-shot retrieval setting. |
BASES: Large-scale Web Search User Simulation with Large Language Model based Agents (2024.findings-emnlp)
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| Challenge: | Existing research on web search rely on real-user experiments, which can be costly to scale up. |
| Approach: | They propose a user simulation framework with LLM-based agents that can generate unique user profiles at scale. |
| Outcome: | The proposed framework can generate unique user profiles at scale, leading to diverse search behaviors. |
RADAR: Risk-Aware Distilled Adaptive Routing for Efficient Short-Form Video Platform Ecosystem Governance (2026.acl-industry)
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| Challenge: | Existing solutions to address inefficiency in large-scale integrity enforcement on short-form video platforms require multiple specialized vertical modules . |
| Approach: | They propose a lightweight risk-aware routing framework that selectively releases low-risk content while dispatching high-risk instances to appropriate vertical modules. |
| Outcome: | The proposed framework selectively releases low-risk content while dispatching high-risk instances to appropriate vertical modules. |
Vision-and-Language Navigation: A Survey of Tasks, Methods, and Future Directions (2022.acl-long)
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| Challenge: | Vision-and-Language Navigation (VLN) is a research topic that is gaining attention in the field of artificial intelligence. |
| Approach: | They propose to build an embodied agent that can communicate with humans in natural language and navigate in real 3D environments. |
| Outcome: | This paper reviews current studies in the emerging field of vision-and-language navigation . it highlights limitations and opportunities for future work . |
Label-Specific Document Representation for Multi-Label Text Classification (D19-1)
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| Challenge: | Existing methods to classify documents using labels only assign one label to document . multi-label text classification is a challenging task because of the huge amount of documents, words and labels. |
| Approach: | They propose a Label-Specific Attention Network (LSAN) to learn a label-specific document representation. |
| Outcome: | The proposed model outperforms state-of-the-art methods on four datasets . it can predict low-frequency labels, and it can be used in sentimental analysis . |
The BQ Corpus: A Large-scale Domain-specific Chinese Corpus For Sentence Semantic Equivalence Identification (D18-1)
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| Challenge: | Bank Question corpus is a corpus for sentence semantic equivalence identification (SSEI) because of rich expressions in natural languages, SSEI is really a challenging task. |
| Approach: | They propose to cluster 120,000 question pairs from 1-year online bank custom service logs into stacks by the Word Mover’s Distance (WMD) based Affinity Propagation algorithm to achieve questions with the same intent. |
| Outcome: | The proposed method achieves questions with the same intent by clustering deduplicated questions into stacks by the Word Mover’s Distance (WMD) based Affinity Propagation (AP) algorithm. |
DAPE V2: Process Attention Score as Feature Map for Length Extrapolation (2025.acl-long)
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Chuanyang Zheng, Yihang Gao, Han Shi, Jing Xiong, Jiankai Sun, Jingyao Li, Minbin Huang, Xiaozhe Ren, Michael Ng, Xin Jiang, Zhenguo Li, Yu Li
| Challenge: | Extensive experiments demonstrate that treating attention as a feature map and applying convolution as . a processing method significantly enhances Transformer performance. |
| Approach: | They propose to use the convolution operator to mimic the processing methods in computer vision to treat attention as a feature map and apply it to neighboring attention scores across different heads. |
| Outcome: | The proposed model can be adapted to various attention-related models and achieves high performance. |
R2H: Building Multimodal Navigation Helpers that Respond to Help Requests (2023.emnlp-main)
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| Challenge: | Existing dialog-based embodied datasets are not sufficient to develop intelligent navigation-helper agents capable of navigating users in unfamiliar areas. |
| Approach: | They introduce a novel benchmark, Respond to Help Requests, to promote the development of multi-modal navigation helpers capable of responding to requests for help . they also propose two approaches to construct the navigation-helper agent, including fine-tuning a task-oriented multi-mod response generation model that can see and respond, named SeeRee, and employing . a multi-module large language model in a zero-shot manner. |
| Outcome: | The proposed model outperforms the baseline model and the proposed model on two tasks based on human evaluations and automatic benchmarking. |