Papers by Zhiqiang Chen

TextLap: Customizing Language Models for Text-to-Layout Planning (2024.findings-emnlp)

Challenge:	Current temporal knowledge graph question answering methods focus on implicit temporal constraints and lack the capability to handle complex temporal queries.
Approach:	They propose a temporal knowledge graph question answering framework that recursively decomposes questions into sub-problems and employs multi-path answer aggregation to improve fault tolerance.
Outcome:	The proposed framework outperforms existing methods on multiTQ and TimelineKGQA benchmarks.

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Jian Chen, Ruiyi Zhang, Yufan Zhou, Jennifer Healey, Jiuxiang Gu, Zhiqiang Xu, Changyou Chen

SKA-Bench: A Fine-Grained Benchmark for Evaluating Structured Knowledge Understanding of LLMs (2025.findings-emnlp)

Challenge:	Creating 2D graphical layouts from text alone is challenging in traditional settings.
Approach:	They propose to customize LLMs to allow users to generate professional looking layouts by simply inputting text instructions.
Outcome:	The proposed method outperforms existing benchmarks for document generation and graphical design benchmarks.

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Zhiqiang Liu, Enpei Niu, Yin Hua, Mengshu Sun, Lei Liang, Huajun Chen, Wen Zhang

Temp-R1: A Unified Autonomous Agent for Complex Temporal KGQA via Reverse Curriculum Reinforcement Learning (2026.acl-long)

Challenge:	Existing evaluations for Structured Knowledge (SK) understanding are non-rigorous and focus on a single type of SK.
Approach:	They propose a structured knowledge understanding benchmark that includes four widely used structured knowledge forms.
Outcome:	The proposed benchmark is based on four widely used structured knowledge forms . it includes a question, an answer, positive knowledge units, and noisy knowledge units .

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Zhaoyan Gong, Zhiqiang Liu, Songze Li, Xiaoke Guo, Yuanxiang Liu, Xinle Deng, Zhizhen Liu, Lei Liang, Huajun Chen, Wen Zhang

Reasoning

Challenge:	Existing methods rely on fixed workflows and expensive closed-source APIs, limiting flexibility and scalability.
Approach:	They propose a temporal reasoning agent that trains on difficult questions first . they expand the action space with specialized internal actions alongside external action .
Outcome:	The proposed agent improves 19.8% over baselines on complex questions and multi-tasks.

Knowledge-augmented Financial Market Analysis and Report Generation (2024.emnlp-industry)

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Yuemin Chen, Feifan Wu, Jingwei Wang, Hao Qian, Ziqi Liu, Zhiqiang Zhang, Jun Zhou, Meng Wang

Generative Annotation for ASR Named Entity Correction (2025.emnlp-main)

Challenge:	Existing methods to generate financial market analysis text require extensive financial knowledge and skill of financial analysts.
Approach:	They propose a task to generate financial market analysis reports using financial market data using a financial knowledge graph.
Outcome:	The proposed framework outperforms large-scale language models and retrieval-augmented baselines in the financial market analysis generation task.

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Yuanchang Luo, Daimeng Wei, Shaojun Li, Hengchao Shang, Jiaxin Guo, Zongyao Li, Zhanglin Wu, Xiaoyu Chen, Zhiqiang Rao, Jinlong Yang, Hao Yang

Learning to Plan for Retrieval-Augmented Large Language Models from Knowledge Graphs (2024.findings-emnlp)

Challenge:	Existing named entity correction models fail to transcribe domain-speciffcnamed entities when theforms of the wrongly-transcribed words and the ground-truth entity are signiffcantly different.
Approach:	They propose a method that utilizes speech sound features to retrieve candidate entities . it uses speech sound feature to annotate entityerrors in ASR transcripts .
Outcome:	The proposed method can bring signiffcant improvement to entity accuracy.

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Junjie Wang, Mingyang Chen, Binbin Hu, Dan Yang, Ziqi Liu, Yue Shen, Peng Wei, Zhiqiang Zhang, Jinjie Gu, Jun Zhou, Jeff Pan, Wen Zhang, Huajun Chen

CRAB: Cross-environment Agent Benchmark for Multimodal Language Model Agents (2025.findings-acl)

Challenge:	Recent studies have attempted to enhance the performance of large language models (LLMs) in complex question-answering (QA) tasks by combining step-wise planning with external retrieval.
Approach:	They propose a framework for enhancing LLMs’ planning capabilities by using planning data derived from knowledge graphs (KGs).
Outcome:	The proposed framework improves LLMs’ planning capabilities by using knowledge graphs (KGs) the proposed framework is compared with existing frameworks on multiple datasets and shows that it is effective for large language models.

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Tianqi Xu, Linyao Chen, Dai-Jie Wu, Yanjun Chen, Zecheng Zhang, Xiang Yao, Zhiqiang Xie, Yongchao Chen, Shilong Liu, Bochen Qian, Anjie Yang, Zhaoxuan Jin, Jianbo Deng, Philip Torr, Bernard Ghanem, Guohao Li

Selection and Generation: Learning towards Multi-Product Advertisement Post Generation (2020.emnlp-main)

Challenge:	Existing benchmarks for MLM agents in interactive environments are limited by their focus on a single environment, lack of detailed and generalized evaluation methods, and the complexity of constructing tasks and evaluators.
Approach:	They propose a cross-environment agent benchmark framework that integrates graph-based evaluation and task generation methods.
Outcome:	The proposed framework supports multiple devices and can be easily extended to any environment with a Python interface.

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Zhangming Chan, Yuchi Zhang, Xiuying Chen, Shen Gao, Zhiqiang Zhang, Dongyan Zhao, Rui Yan

Text Generation

Challenge:	E-commerce websites have billions of products, so it is impossible to write all copywriting manually.
Approach:	They propose a model to generate an AD post using a select network and a MGenNet network to generate a post including selected products.
Outcome:	The proposed model achieves impressive performance on a large-scale real-world AD post dataset.

ASTRA: Adaptive Semantic Tree Reasoning Architecture for Complex Table Question Answering (2026.acl-long)

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Xiaoke Guo, Songze Li, Zhiqiang Liu, Zhaoyan Gong, Yuanxiang Liu, Huajun Chen, Wen Zhang

Learning Disentangled Semantic Representations for Zero-Shot Cross-Lingual Transfer in Multilingual Machine Reading Comprehension (2022.acl-long)

Challenge:	Existing serialization methods fail to capture explicit hierarchies and lack schema flexibility . Existing tree-based approaches suffer from limited semantic adaptability .
Approach:	They propose a method that leverages the global semantic awareness of LLMs to reconstruct tables into Logical Semantic Trees.
Outcome:	The proposed method achieves state-of-the-art (SOTA) performance on complex table benchmarks.

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Linjuan Wu, Shaojuan Wu, Xiaowang Zhang, Deyi Xiong, Shizhan Chen, Zhiqiang Zhuang, Zhiyong Feng

BOSE: A Systematic Evaluation Method Optimized for Base Models (2025.findings-acl)

Challenge:	Existing methods to zero-shot transfer knowledge from rich-resource to low-resourced languages are limited due to linguistic discrepancies in different languages.
Approach:	They propose a multilingual MRC framework equipped with a Siamese Semantic Disentanglement Model to disassociate semantics from syntax in models learned by multilingual pre-trained models.
Outcome:	The proposed model disassociates semantics from syntax in multilingual models.

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Hongzhi Luan, Changxin Tian, Zhaoxin Huan, Xiaolu Zhang, Kunlong Chen, Zhiqiang Zhang, Jun Zhou

What’s Missing in Screen-to-Action? Towards a UI-in-the-Loop Paradigm for Multimodal GUI Reasoning (2026.findings-acl)

Challenge:	Existing evaluation methods for large language models (LLMs) are inadequate to provide solid conclusions for key experiments such as data ablation and scaling law.
Approach:	They propose a method specifically designed to optimize the evaluation of base models by incorporating two innovations: In-Context Light-instruction Prompt and Blank-ppl for multi-choice tasks with candidate options.
Outcome:	The proposed method significantly improves stability and consistency of evaluations during pre-training and consistency between base and instruct models.

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Songze Li, Xiaoke Guo, Tianqi Liu, Biao Yi, Zhaoyan Gong, Zhiqiang Liu, Huajun Chen, Wen Zhang

Responsible & Trustworthy NLP

Challenge:	Existing GUI reasoning methods rely on direct screen-based decision-making, which lacks interpretability and overlooks a comprehensive understanding of UI elements, ultimately leading to task failure.
Approach:	They propose a GUI reasoning paradigm that treats the GUI reasoning task as a cyclic *Screen-UI elements-Action process.
Outcome:	The proposed paradigm achieves state-of-the-art UI understanding performance while yielding superior results in GUI reasoning tasks.

Adapter-TST: A Parameter Efficient Method for Multiple-Attribute Text Style Transfer (2023.findings-emnlp)

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Zhiqiang Hu, Nancy Chen, Roy Lee

ConvFinQA: Exploring the Chain of Numerical Reasoning in Conversational Finance Question Answering (2022.emnlp-main)

Challenge:	Existing studies explore performing text style transfer on attributes like age, gender, formality, politeness, and formality.
Approach:	They propose a framework that freezes the pre-trained model’s original parameters and enables the development of a multiple-attribute text style transfer model.
Outcome:	The proposed model outperforms state-of-the-art models on sentiment transfer and multiple-attribute transfer tasks with significantly less computational resources.

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Zhiyu Chen, Shiyang Li, Charese Smiley, Zhiqiang Ma, Sameena Shah, William Yang Wang

Natural Language Interfaces

Challenge:	Recent advances in large pre-trained language models have brought the NLP field into a new era.
Approach:	They propose a large-scale dataset to study the chain of numerical reasoning in conversational question answering.
Outcome:	The proposed dataset should push forward the exploration of real-world, complex reasoning tasks as the next research focus.

Have We Designed Generalizable Structural Knowledge Promptings? Systematic Evaluation and Rethinking (2025.acl-long)

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Yichi Zhang, Zhuo Chen, Lingbing Guo, Yajing Xu, Shaokai Chen, Mengshu Sun, Binbin Hu, Zhiqiang Zhang, Lei Liang, Wen Zhang, Huajun Chen

Crossroads of Optimization under Uncertainty: How to Choose the Optimal Model (2026.findings-acl)

Challenge:	Existing methods to integrate external knowledge into LLMs focus on specific problems, lacking a comprehensive exploration of the generalization and capability boundaries of SKP.
Approach:	They propose a new paradigm for structural knowledge prompting to integrate external structural knowledge into LLMs by incorporating structural representations.
Outcome:	The proposed benchmark SUBARU enables the evaluation of the generalization capabilities of SKP from four perspectives.

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Chengxi She, Zhiqiang Chen, Xingyu Lu, Caihua Chen, Piaoyang Zhao, Xuedong Wang

Challenge:	Existing approaches to Optimization under Uncertainty (OuU) have inherent limitations and advantages.
Approach:	They propose a framework that automates the modeling and solving of six types of uncertainty models and generates mapping pairs to explore the potential relationship between optimization problems and optimal models.
Outcome:	The proposed framework achieves superior performance even on specific model types, with correlation analysis showing that data scale and specific scenario significantly influence model selection.

DRAG: Distilling RAG for SLMs from LLMs to Transfer Knowledge and Mitigate Hallucination via Evidence and Graph-based Distillation (2025.acl-long)

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Jennifer Chen, Aidar Myrzakhan, Yaxin Luo, Hassaan Muhammad Khan, Sondos Mahmoud Bsharat, Zhiqiang Shen

Responsible & Trustworthy NLP

Challenge:	Large-scale RAG systems consume significant computational resources and are prone to generating “hallucinated” content from Humans.
Approach:	They propose a framework for distilling RAG knowledge from large-scale language models into small LMs.
Outcome:	The proposed method outperforms the prior competitive RAG methods like MiniRAG for SLMs by up to 27.7% using the same models, preserving high-level efficiency and reliability.

Visual Question Decomposition on Multimodal Large Language Models (2024.findings-emnlp)

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Haowei Zhang, Jianzhe Liu, Zhen Han, Shuo Chen, Bailan He, Volker Tresp, Zhiqiang Xu, Jindong Gu

Reflective RAG: Self-Evaluation Driven Strategy Optimization in Agentic Retrieval-Augmented Generation (2026.findings-acl)

Challenge:	Existing methods for question decomposition focus on unimodal language models, but question decomposing capability of Multimodal Large Language Models (MLLMs) has yet to be explored.
Approach:	They propose a finetuning dataset and a training objective for selective decomposition to enhance the model's question decomposing capability.
Outcome:	The proposed dataset shows that existing models struggle to produce high-quality sub-questions.

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Haiyan Wu, Chenchen Wang, Chaoqun Sun, Chengxiong Lu, Yan-Hong Chen, Zhiqiang Zhang, Xiaoqing Feng

Information Retrieval

Challenge:	Recent agentic RAG systems lack the capacity to evaluate the utility of retrieved information, leading to brittle reasoning and suboptimal decision-making.
Approach:	They propose a framework that integrates self-evaluation to dynamically optimize retrieval and generation strategy.
Outcome:	The proposed framework outperforms strong agentic baselines on five knowledge-intensive QA benchmarks and improves training stability and generalization to multi-hop reasoning tasks.

Beyond Completion: A Foundation Model for General Knowledge Graph Reasoning (2025.findings-acl)

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Yin Hua, Zhiqiang Liu, Mingyang Chen, Zheng Fang, Chi Man Wong, Lingxiao Li, Chi Man Vong, Huajun Chen, Wen Zhang

Structured Data in NLP

Challenge:	Existing foundation models for general knowledge graph reasoning have focused on their structural aspects, with most efforts restricted to in-KG tasks.
Approach:	They propose a conditional encoding architecture that bridges the gap between textual and structural modalities, enabling seamless integration.
Outcome:	The proposed model outperforms baseline models on 28 datasets and is generalized to out-of-KG tasks.

Zero-Shot Cross-Lingual Document-Level Event Causality Identification with Heterogeneous Graph Contrastive Transfer Learning (2024.lrec-main)

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Zhitao He, Pengfei Cao, Zhuoran Jin, Yubo Chen, Kang Liu, Zhiqiang Zhang, Mengshu Sun, Jun Zhao

OneGen: Efficient One-Pass Unified Generation and Retrieval for LLMs (2024.findings-emnlp)

Challenge:	Existing studies focus on sentence-level ECI with high-resource languages, leaving document-level DECI with low-resourced languages under-explored.
Approach:	They propose a Heterogeneous Graph Interaction Model with Multi-granularity Contrastive Transfer Learning for zero-shot cross-lingual ECI.
Outcome:	The proposed model outperforms the state-of-the-art model on monolingual and multilingual scenarios by 9.4% and 8.2% of average F1 score.

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Jintian Zhang, Cheng Peng, Mengshu Sun, Xiang Chen, Lei Liang, Zhiqiang Zhang, Jun Zhou, Huajun Chen, Ningyu Zhang

Green & Sustainable NLP

Challenge:	Recent advances in Large Language Models (LLMs) have significantly enhanced the generative capabilities for various NLP tasks, but they still suffer from hallucinations due to their exclusive reliance on parametric knowledge.
Approach:	They propose a framework that integrates retrieval tokens generated autoregressively into a single LLM to handle both tasks simultaneously in a unified forward pass.
Outcome:	The proposed framework bridges the traditionally separate training approaches for generation and retrieval by incorporating retrieval tokens generated autoregressively.

SeaLLMs - Large Language Models for Southeast Asia (2024.acl-demos)

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Xuan-Phi Nguyen, Wenxuan Zhang, Xin Li, Mahani Aljunied, Zhiqiang Hu, Chenhui Shen, Yew Ken Chia, Xingxuan Li, Jianyu Wang, Qingyu Tan, Liying Cheng, Guanzheng Chen, Yue Deng, Sen Yang, Chaoqun Liu, Hang Zhang, Lidong Bing

Enrich-on-Graph: Query-Graph Alignment for Complex Reasoning with LLM Enriching (2025.emnlp-main)

Challenge:	Existing large language models favor high-resource languages, such as English, at the expense of low-resourced and regional languages.
Approach:	They propose a series of language models that specifically focuses on Southeast Asian languages.
Outcome:	SeaLLM models outperform ChatGPT-3.5 in non-Latin languages by large margins . linguistic disparity impedes access to state-of-the-art AI technologies for non-English-speaking populations .

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Songze Li, Zhiqiang Liu, Zhengke Gui, Huajun Chen, Wen Zhang

Structured Data in NLP

Challenge:	Existing methods employ resource-intensive, non-scalable workflows reasoning on vanilla KGs, but overlook this gap.
Approach:	They propose a flexible framework that leverages LLMs’ prior knowledge to enrich KGs and bridge the semantic gap between queries and graphs.
Outcome:	The proposed framework bridges the semantic gap between structured knowledge graphs and unstructured queries while ensuring low computational costs, scalability, and adaptability across different methods.

Knowledge Graph Pooling and Unpooling for Concept Abstraction (2025.coling-main)

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Juan Li, Wen Zhang, Zhiqiang Liu, Mingchen Tu, Mingyang Chen, Ningyu Zhang, Shijian Li

Structured Data in NLP

Challenge:	Knowledge graph embedding (KGE) aims to embed entities and relations as vectors in a continuous space.
Approach:	They propose a framework with KG Pooling and unpooling and Contrastive Learning to abstract and encode latent concepts for better KG prediction.
Outcome:	The proposed framework outperforms baselines on link prediction task.

Croppable Knowledge Graph Embedding (2025.acl-long)

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Yushan Zhu, Wen Zhang, Zhiqiang Liu, Mingyang Chen, Lei Liang, Huajun Chen

Combining the Best of Both Worlds: A Method for Hybrid NMT and LLM Translation (2025.findings-acl)

Challenge:	Knowledge Graph Embedding (KGE) is a common approach for Knowledge Grasse (KGs) in AI tasks.
Approach:	They propose a new KGE training framework MED that allows one training to obtain a croppable KGE model for multiple scenarios with different dimensional needs.
Outcome:	The proposed framework improves low-dimensional sub-models and makes high-dimensional models retain the low-dimension sub-modells’ capacity.

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Zhanglin Wu, Daimeng Wei, Xiaoyu Chen, Hengchao Shang, Jiaxin Guo, Zongyao Li, Yuanchang Luo, Jinlong Yang, Zhiqiang Rao, Hao Yang

Multilinguality

Challenge:	Large language models have advantages over neural machine translation systems, but they suffer from high computational costs and significant latency.
Approach:	They propose a scheduling policy that optimizes translation result while ensuring fast speed and as little LLM usage as possible.
Outcome:	The proposed model achieves optimal translation performance with less LLM usage on multilingual test sets.

From Style to Story: A Curriculum Learning Approach for Imitative Novel Generation (2026.findings-acl)

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Xueran Han, Yuhan Liu, Mingzhe Li, Wei Liu, Sen Hu, Rui Yan, Zhiqiang xu, Xiuying Chen

Stick to the Facts: Learning towards a Fidelity-oriented E-Commerce Product Description Generation (D19-1)

Challenge:	Novels create rich, immersive worlds with intricate plots and distinct styles, captivating readers through complex storytelling.
Approach:	They propose a novel generation system that imitates novel elements by predicting plot developments and writing concrete details using vivid, expressive language.
Outcome:	The novel imitative novel generation system is trained through a curriculum learning paradigm, progressing from low-level stylistic mastery to high-level narrative coherence.

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Zhangming Chan, Xiuying Chen, Yongliang Wang, Juntao Li, Zhiqiang Zhang, Kun Gai, Dongyan Zhao, Rui Yan

Text Generation

Challenge:	Existing models for product description generation do not take the product attribute information into account.
Approach:	They propose a model that takes the embedding and the entity label of each word into account . they establish a keyword memory that stores the entity labels as keys and keywords as values .
Outcome:	The proposed model increases the fidelity of the generated descriptions by 25%.

FinEval: A Chinese Financial Domain Knowledge Evaluation Benchmark for Large Language Models (2025.naacl-long)

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Xin Guo, Haotian Xia, Zhaowei Liu, Hanyang Cao, Zhi Yang, Zhiqiang Liu, Sizhe Wang, Jinyi Niu, Chuqi Wang, Yanhui Wang, Xiaolong Liang, Xiaoming Huang, Bing Zhu, Zhongyu Wei, Yun Chen, Weining Shen, Liwen Zhang

TagRouter: Learning Route to LLMs through Tags for Open-Domain Text Generation Tasks (2025.findings-acl)

Challenge:	Large language models have demonstrated outstanding performance in various natural language processing tasks, but their security capabilities in the financial domain have not been explored.
Approach:	They propose to use a benchmark to evaluate large language models' financial domain knowledge and practical abilities.
Outcome:	The proposed benchmark evaluates large language models' financial domain knowledge and practical abilities.

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Zhou Chen, Zhiqiang Wei, Yuqi Bai, Xue Xiong, Jianmin Wu

Green & Sustainable NLP

Challenge:	Existing models with limited performance and limited training can be difficult to use in large-scale applications.
Approach:	They propose a training-free model routing method that optimizes synergy among multiple LLMs for open-domain text generation tasks.
Outcome:	The proposed method outperforms 13 baseline models and reduces costs by 17.20%.

Visual Self-Refinement for Autoregressive Models (2025.findings-emnlp)

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Jiamian Wang, Ziqi Zhou, Chaithanya Kumar Mummadi, Sohail Dianat, Majid Rabbani, Raghuveer Rao, Chen Qiu, Zhiqiang Tao