Papers by Xin Shi

Program Transfer for Answering Complex Questions over Knowledge Bases (2022.acl-long)

Challenge:	Existing domain-adaptive pre-training (DAPT) models tend to forget the general knowledge acquired by general PLMs, leading to catastrophic forgetting and sub-optimal performance.
Approach:	They propose a framework which augments the domain-specific PLM by a memory built from the frozen general PLM without losing the general knowledge.
Outcome:	The proposed framework augments the domain-specific PLM by a memory built from the frozen general PLM without losing the general knowledge.

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Shulin Cao, Jiaxin Shi, Zijun Yao, Xin Lv, Jifan Yu, Lei Hou, Juanzi Li, Zhiyuan Liu, Jinghui Xiao

Knowledge Representation

Challenge:	Program induction for complex questions over knowledge bases relies on a large number of parallel question-program pairs for the given KB, but the gold program annotations are usually lacking, making learning difficult.
Approach:	They propose an approach to leverage program annotations on rich KBs as external supervision signals to aid program induction for low-resourced KB.
Outcome:	The proposed approach outperforms SOTA methods on ComplexWebQuestions and WebQuestionSP.

On the Role of Entity and Event Level Conceptualization in Generalizable Reasoning: A Survey of Tasks, Methods, Applications, and Future Directions (2025.findings-emnlp)

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Weiqi Wang, Tianqing Fang, Haochen Shi, Baixuan Xu, Wenxuan Ding, Liyu Zhang, Wei Fan, Jiaxin Bai, Haoran Li, Xin Liu, Yangqiu Song

Reasoning

Challenge:	Conceptualization is a fundamental element of human cognition and plays a pivotal role in generalizable reasoning.
Approach:	They propose to categorize different types of conceptualizations into four levels based on the types of instances being conceptualized.
Outcome:	The proposed categorization of different types of conceptualizations into four levels based on the types of instances being conceptualized .

Improving Knowledge Graph Embedding Using Affine Transformations of Entities Corresponding to Each Relation (2021.findings-emnlp)

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Jinfa Yang, Yongjie Shi, Xin Tong, Robin Wang, Taiyan Chen, Xianghua Ying

UltraEval-Audio: A Unified Framework for Comprehensive Evaluation of Audio Foundation Models (2026.acl-demo)

Challenge:	Existing knowledge graph embedding methods use k-dimensional vectors to represent each entity in a knowledge graph.
Approach:	They propose to use affine transformations to embed knowledge graphs using previous methods . they propose to add k additional variables to the existing methods to perform embedding .
Outcome:	The proposed method outperforms RotatE, Distmult and ComplEx on various data sets.

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Qundong Shi, Jie Zhou, Biyuan Lin, Junbo Cui, Guoyang Zeng, Yixuan Zhou, Ziyang Wang, Xin Liu, Zhen Luo, Yudong Wang, Zhiyuan Liu

InsLogicBench: An Argumentation Logic Grounded Benchmark for Complex Insurance Claims Adjudication (2026.acl-long)

Challenge:	Existing evaluation frameworks for audio foundation models are heavily reliant on English, making it difficult to objectively assess models’ performance on Chinese.
Approach:	They propose a unified framework that supports 10 languages, 14 task categories, 24 models, and 36 benchmarks with one-command evaluation and real-time leaderboards.
Outcome:	The proposed framework supports 10 languages, 14 task categories, 24 models, and 36 benchmarks with one-command evaluation and real-time leaderboards.

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Jin Liu, Yunpeng Liu, Keyi Wang, Jie Shi, Xiao Xu, Wenkang Huang, Xingzhong Xu, Xin Liang, Yanghua Xiao

Responsible & Trustworthy NLP

Challenge:	Existing benchmarks for insurance claims adjudication are limited to information retrieval or simple multiple-choice setups.
Approach:	They propose a benchmark that provides complete reasoning traces linking factual inputs, relevant policy clauses, and final verdicts.
Outcome:	The proposed benchmark shows that models often produce correct decisions but fail to provide precise justifications, highlighting a critical discrepancy between decision accuracy and logical reasoning capabilities.

Counteracting the Matthew Effect in Self-Improvement of LVLMs through Head-Tail Re-balancing (2026.acl-long)

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Xin Guo, Zhiheng Xi, Yiwen Ding, Yitao Zhai, Xiaowei Shi, Xunliang Cai, Tao Gui, Qi Zhang, Xuanjing Huang

Probabilistic Tree-of-thought Reasoning for Answering Knowledge-intensive Complex Questions (2023.findings-emnlp)

Challenge:	Large vision language models have impressive reasoning capabilities across complex multimodal tasks.
Approach:	They propose to use distribution-reshaping and trajectory-rebalancing to improve visual reasoning capabilities.
Outcome:	Experiments on Qwen2-VL-7B-Instruct and InternVL2.5-4B models show that their methods outperform baselines by 3.86 points.

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Shulin Cao, Jiajie Zhang, Jiaxin Shi, Xin Lv, Zijun Yao, Qi Tian, Lei Hou, Juanzi Li

Reasoning

Challenge:	Large language models (LLMs) are capable of answering knowledge-intensive complex questions with chain-of-thought reasoning.
Approach:	They propose a method to solve complex questions with a tree-of-thought approach using parametric knowledge and retrieved external knowledge to augment CoT reasoning.
Outcome:	The proposed approach outperforms SOTA methods on three Complex QA datasets under the open-domain setting.

QADYNAMICS: Training Dynamics-Driven Synthetic QA Diagnostic for Zero-Shot Commonsense Question Answering (2023.findings-emnlp)

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Haochen Shi, Weiqi Wang, Tianqing Fang, Baixuan Xu, Wenxuan Ding, Xin Liu, Yangqiu Song

Knowledge Graph Embedding by Adaptive Limit Scoring Loss Using Dynamic Weighting Strategy (2022.findings-acl)

Challenge:	Existing approaches to QA fine-tune language models on QA pairs constructed from CommonSense Knowledge Bases (CSKBs) however, current QA synthesis protocols introduce noise from the CSKB and generate ungrammatical questions and false negative options, which impede the model’s ability to generalize.
Approach:	They propose a framework to analyze the training dynamics of each QA pair at both the question level and option level, discarding machine-detectable artifacts and mislabeled or false-negative options.
Outcome:	The proposed framework outperforms baseline approaches while using only 33% of the synthetic data.

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Jinfa Yang, Xianghua Ying, Yongjie Shi, Xin Tong, Ruibin Wang, Taiyan Chen, Bowei Xing

Leveraging Unpaired Feedback for Long-Term LLM-based Recommendation Tuning (2025.findings-emnlp)

Challenge:	Existing knowledge graph embedding models use a loss framework to distinguish between correct and incorrect triplets.
Approach:	They propose a loss framework that reweights each triplet to highlight the less-optimized triplets.
Outcome:	The proposed method performs on several knowledge graph embedding models, including TransE, TransH and ComplEx.

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Jizhi Zhang, Chongming Gao, Wentao Shi, Xin Chen, Jingang Wang, Xunliang Cai, Fuli Feng

Challenge:	a recent study highlights unpaired feedback as a key challenge for long-term LLM-based recommenders . unpaired user feedback is crucial for improving LLMs in dynamic user environments, authors say .
Approach:	They propose a framework that incorporates unpaired feedback into LLMs to improve long-term recommendation performance.
Outcome:	The proposed framework improves long-term recommendation performance by incorporating unpaired feedback without requiring paired supervision.

ToolPRM: Fine-Grained Inference Scaling of Structured Outputs for Function Calling (2026.acl-long)

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Jianghao Lin, Yuanyuan Shi, Xin Peng, Renjie Ding, Hairui Wang, Yuxuan Peng, Bizhe Bai, Weixi Song, Fengshuo Bai, Huacan Chai, Weinan Zhang, Fei Huang, Ying Wen

Challenge:	Existing research on inference scaling focuses on unstructured output generation tasks, such as mathematical problems.
Approach:	They propose an inference-scaling framework that combines fine-grained beam search with ToolPRM, a process reward model scoring each intra-call decision.
Outcome:	The proposed framework outperforms outcome and coarse-grained reward models in predictive accuracy and yields consistent test-time gains on multiple function-calling benchmarks.

Event Causality Extraction with Event Argument Correlations (2022.coling-1)

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Shiyao Cui, Jiawei Sheng, Xin Cong, Quangang Li, Tingwen Liu, Jinqiao Shi

Syntactic Text Processing

Challenge:	Event Causality Identification (ECI) ignores crucial event structure and cause-effect component information, making it struggle for downstream applications.
Approach:	They propose a task to extract event causality pairs with their structured event information from plain text.
Outcome:	The proposed method captures the intra- and inter-event argument correlations for ECE and provides several future directions.

Video2Roleplay: A Multimodal Dataset and Framework for Video-Guided Role-playing Agents (2025.emnlp-main)

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Xueqiao Zhang, Chao Zhang, Jingtao Xu, Yifan Zhu, Xin Shi, Yi Yang, Yawei Luo

MASTER: Multi-Agent Security Through Exploration of Roles and Topological Structures - A Comprehensive Framework (2025.findings-emnlp)

Challenge:	Existing approaches to RPAs focus on static role profiles, overlooking dynamic perceptual abilities inherent to humans.
Approach:	They propose a framework that combines adaptive temporal sampling with dynamic and static role profiles.
Outcome:	The proposed framework combines adaptive temporal sampling with dynamic and static role profiles.

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Yifan Zhu, Chao Zhang, Xin Shi, Xueqiao Zhang, Yi Yang, Yawei Luo

Robustness in NLP

Challenge:	Large Language Models (LLMs)-based Multi-Agent Systems (MAS) exhibit remarkable problem-solving and task planning capabilities across diverse domains .
Approach:	They propose a security research framework for LLM-based multi-agent systems . they propose corresponding defense strategies to address MAS security risks .
Outcome:	The proposed framework amplifies the severity of security risks under MAS attacks . it offers an automated construction process for different MAS setups and an interaction paradigm .

SeCuRepair: Semantics-Aligned, Curriculum-Driven, and Reasoning-Enhanced Vulnerability Repair Framework (2026.acl-long)

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Chengran Yang, Ting Zhang, Jinfeng Jiang, Xin Zhou, Haoye Tian, Mingzhe Du, Jieke Shi, Junkai Chen, Yikun Li, Eng Lieh Ouh, Lwin Khin Shar, David Lo

Reasoning

Challenge:	Existing methods for automating vulnerability repair suffer from syntactic overfitting . nvd published 49,230 Common Vulnerabilities and Exposures (CVE) records in 2025 alone .
Approach:	They propose a semantic-aware reward framework that optimizes for code semantic equivalence rather than lexical mimicry.
Outcome:	The proposed framework outperforms state-of-the-art frameworks on repository-level splits . it incorporates expert-aligned reasoning mechanism that grounds patch generation in structured diagnosis.

AbsPyramid: Benchmarking the Abstraction Ability of Language Models with a Unified Entailment Graph (2024.findings-naacl)

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Zhaowei Wang, Haochen Shi, Weiqi Wang, Tianqing Fang, Hongming Zhang, Sehyun Choi, Xin Liu, Yangqiu Song

QuickLLaMA: Query-aware Inference Acceleration for Large Language Models (2025.coling-main)

Challenge:	Existing language models only touch nouns or verbs within simplified events or specific domains.
Approach:	They propose an entailment graph that collects abstract knowledge for 3 components of diverse events to comprehensively evaluate the abstraction ability of language models.
Outcome:	The proposed benchmark improves LLMs across two previous abstraction tasks.

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Jingyao Li, Han Shi, Sitong Wu, Chuanyang Zheng, Zhenguo Li, Xin Jiang, Hong Xu, Jiaya Jia

MatPlotAgent: Method and Evaluation for LLM-Based Agentic Scientific Data Visualization (2024.findings-acl)

Challenge:	Large Language Models (LLMs) struggle with capturing long-distance dependencies within sequences to deeply understand semantics.
Approach:	They propose a system that captures relevant information within a fixed window size and provides precise answers to queries.
Outcome:	The proposed system can read Harry Potter within 30s and accurately answer the questions.

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Zhiyu Yang, Zihan Zhou, Shuo Wang, Xin Cong, Xu Han, Yukun Yan, Zhenghao Liu, Zhixing Tan, Pengyuan Liu, Dong Yu, Zhiyuan Liu, Xiaodong Shi, Maosong Sun

Beyond Pedagogical Principles: Multi-Horizon Preference Optimization for Efficient Socratic Tutoring (2026.acl-long)

Challenge:	Scientific data visualization is an essential process in research, but its use of large language models remains unexplored.
Approach:	They propose a model-agnostic LLM agent framework to automate scientific data visualization tasks.
Outcome:	The proposed framework improves performance of commercial and open-source models.

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Xin Shi, Chao Zhang, Yifan Zhu, Xueqiao Zhang, Yawei Luo

Green & Sustainable NLP

Challenge:	Existing methods for developing LLMs are constrained by static data or sparse reward signals in online settings.
Approach:	They propose a framework that iteratively refines tutor agents using a multi-horizon reward function within a dynamic teacher-student simulation environment.
Outcome:	The proposed framework improves model performance and balances principles and effectiveness compared to baselines.

Reasoning over Hierarchical Question Decomposition Tree for Explainable Question Answering (2023.acl-long)

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Jiajie Zhang, Shulin Cao, Tingjian Zhang, Xin Lv, Juanzi Li, Lei Hou, Jiaxin Shi, Qi Tian

Question Answering

Challenge:	Existing XQA methods focus on reasoning on a single knowledge source, e.g., structured knowledge bases, unstructured corpora, etc. Existing work in XQA focuses on integrating information from heterogeneous knowledge sources.
Approach:	They propose to leverage question decomposing for heterogeneous knowledge integration by breaking down a complex question into simpler ones and selecting the appropriate knowledge source for each sub-question.
Outcome:	The proposed framework outperforms SOTA methods on complex QA datasets.

MASFactory: A Graph-centric Framework for Orchestrating LLM-Based Multi-Agent Systems with Vibe Graphing (2026.acl-demo)

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Yang Liu, Jinxuan Cai, Yishen Li, Qi Meng, Zedi Liu, Xin Li, Chen Qian, Chuan Shi, Cheng Yang

Structured Data in NLP

Challenge:	Large language model-based multi-agent systems (MAS) are increasingly used to extend agentic problem solving via role specialization and collaboration.
Approach:	They propose a graph-centric framework for orchestrating large language model-based multi-agent systems . they compile a user's natural-language intent into an editable workflow specification and then into an executable graph .
Outcome:	The proposed framework compiles natural-language intent into an executable graph and then compile and executes it at runtime.

Construction of Paired Knowledge Graph - Text Datasets Informed by Cyclic Evaluation (2024.lrec-main)

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Ali Mousavi, Xin Zhan, He Bai, Peng Shi, Theodoros Rekatsinas, Benjamin Han, Yunyao Li, Jeffrey Pound, Joshua M. Susskind, Natalie Schluter, Ihab F. Ilyas, Navdeep Jaitly

Structured Data in NLP

Challenge:	Prior studies have shown that sequence-to-sequence models learn to hallucinate when the conditioning data has poor correlation with the sequence being produced.
Approach:	They construct a dataset that pairs Knowledge Graphs (KG) and text together and compare their results to a cyclic evaluation model.
Outcome:	The proposed model performs better on cyclic generation of KGs than on KG-T, but less well on synchronization of KTs.

Learning Hierarchy-Aware Quaternion Knowledge Graph Embeddings with Representing Relations as 3D Rotations (2022.coling-1)

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Jinfa Yang, Xianghua Ying, Yongjie Shi, Xin Tong, Ruibin Wang, Taiyan Chen, Bowei Xing

MIND: Multimodal Shopping Intention Distillation from Large Vision-language Models for E-commerce Purchase Understanding (2024.emnlp-main)

Challenge:	Existing knowledge graph embedding models fail to model semantic hierarchies . Existing methods fail to understand the semantic hierarchies of knowledge graphs .
Approach:	They propose a model which embeds entities as pure quaternions and constrains the modulus of entities to make them have hierarchical distributions.
Outcome:	The proposed model can encode symmetry/antisymmetry, inversion, composition, multiple relation patterns and learn semantic hierarchies simultaneously.

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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

Green & Sustainable NLP

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.

DecoupledESC: Enhancing Emotional Support Generation via Strategy-Response Decoupled Preference Optimization (2025.findings-emnlp)

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Chao Zhang, Xin Shi, Xueqiao Zhang, Yifan Zhu, Yi Yang, Yawei Luo

A Data-Centric Framework for Composable NLP Workflows (2020.emnlp-demos)

Challenge:	Existing ESC data entangles psychological strategies and response content, making it difficult to construct high-quality preference pairs.
Approach:	They propose a Decoupled ESC framework that decomposes the ESC task into two sequential subtasks: strategy planning and empathic response generation.
Outcome:	The proposed framework outperforms baselines, reducing preference bias and improving response quality.

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Zhengzhong Liu, Guanxiong Ding, Avinash Bukkittu, Mansi Gupta, Pengzhi Gao, Atif Ahmed, Shikun Zhang, Xin Gao, Swapnil Singhavi, Linwei Li, Wei Wei, Zecong Hu, Haoran Shi, Xiaodan Liang, Teruko Mitamura, Eric Xing, Zhiting Hu

Challenge:	Empirical natural language processing (NLP) systems involve interoperation among multiple components . a wealth of NLP toolkits exist ( 4), such as spaCy, DKPro, CoreNLP.
Approach:	They propose a unified open-source framework that supports fast development of NLP workflows . framework includes processors for NLP tasks, visualization, and annotation .
Outcome:	The framework offers processors for NLP tasks, visualization, and annotation, and is extensible . it is delivered through two modularized yet integratable open-source projects, Forte and Stave .

CANDLE: Iterative Conceptualization and Instantiation Distillation from Large Language Models for Commonsense Reasoning (2024.acl-long)

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Weiqi Wang, Tianqing Fang, Chunyang Li, Haochen Shi, Wenxuan Ding, Baixuan Xu, Zhaowei Wang, Jiaxin Bai, Xin Liu, Cheng Jiayang, Chunkit Chan, Yangqiu Song

Transformation Networks for Target-Oriented Sentiment Classification (P18-1)

Challenge:	Existing approaches to generalize commonsense reasoning lack instantiated knowledge and require pre-built concept taxonomies and annotations.
Approach:	They propose a framework that iteratively performs contextualized conceptualization and instantiation over commonsense knowledge bases by instructing large language models to generate both types of knowledge with critic filtering.
Outcome:	Empirical results show that distilling CANDLE on student models provides benefits across three downstream tasks.

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Xin Li, Lidong Bing, Wai Lam, Bei Shi

Text Classification

Challenge:	a new model for sentiment classification uses attention instead of attention to classify sentiment polarities over individual opinion targets.
Approach:	They propose a model that uses a CNN layer to extract salient features from transformed word representations from a bi-directional RNN layer.
Outcome:	The proposed model achieves state-of-the-art on a few benchmarks.

Hierarchical Safety Realignment: Lightweight Restoration of Safety in Pruned Large Vision-Language Models (2025.findings-acl)

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Yue Li, Xin Yi, Dongsheng Shi, Gerard De Melo, Xiaoling Wang, Linlin Wang

SAFE-QAQ: End-to-End Slow-Thinking Audio-Text Fraud Detection via Reinforcement Learning (2026.acl-long)

Challenge:	Recent work has shown that pruning can reduce model performance, but it can also lead to degradation in safety performance.
Approach:	They propose a hierarchical safety realignment approach to prune large vision-Language Models . they quantify contribution of each attention head to safety and restore neurons .
Outcome:	The proposed approach achieves significant safety improvements in LVLMs pruned post pruning.

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Peidong Wang, Zhiming Ma, Xin Dai, YongKang Liu, Shi Feng, Xiaocui Yang, Wenxing Hu, Zhihao Wang, Mingjun Pan, Li Yuan, Daling Wang

AI2Agent: An End-to-End Framework for Deploying AI Projects as Autonomous Agents (2025.acl-demo)

Challenge:	Existing methods for fraud detection rely on transcribed text, lacking acoustic cues . a proposed framework for audio-based slow-thinking fraud detection eliminates transcription errors .
Approach:	They propose a framework for audio-based slow-thinking fraud detection that eliminates transcription errors and rewards slow-thought reasoning by capturing fine-grained audio details.
Outcome:	The proposed method improves accuracy, inference efficiency, and real-time processing capabilities.

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Jiaxiang Chen, Jingwei Shi, Lei Gan, Jiale Zhang, Qingyu Zhang, Dongqian Zhang, Pang Xin, Zhucong Li, Xu Yinghui

BatonVoice: An Operationalist Framework for Enhancing Controllable Speech Synthesis with Linguistic Intelligence from LLMs (2026.acl-long)

Challenge:	a new framework automates deployment and debugging of AI projects . complexity of environment configurations, dependency conflicts, and debuggering issues hinder scalability and adoption.
Approach:	They propose an end-to-end framework that automates AI project deployment . they conducted experiments on 30 AI deployment cases to evaluate its effectiveness .
Outcome:	The proposed framework reduces deployment time and improves success rates by reducing human intervention.

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Yue Wang, Ruotian Ma, Xingyu Chen, Zhengliang Shi, Morunliu Yang, Wanshun Chen, Huang Liu, Jiadi Yao, Xin He, Qu Yang, Qingxuan Jiang, Fanghua Ye, Juntao Li, Zhaopeng Tu, Xiaolong Li, Liefeng Bo, Min Zhang

Speech & Audio in NLP

Challenge:	Existing approaches often fail to leverage the linguistic intelligence of Large Language Models (LLMs) Existing models lack the ability to follow text instructions for controllable Text-to-Speech (TTS).
Approach:	They propose a framework where an LLM acts as a conductor, understanding user instructions and generating a textual plan - explicit vocal features.
Outcome:	The proposed model outperforms open- and closed-source models in speech synthesis and achieves zero-shot cross-lingual generalization.

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