Papers by Zhiqiang Li

Rethinking Sample Polarity in Reinforcement Learning with Verifiable Rewards (2026.acl-long)

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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Xinyu Tang, Yuliang Zhan, Zhixun Li, Xin Zhao, Zhenduo Zhang, Zujie Wen, Zhiqiang Zhang, Jun Zhou

Reasoning

Challenge:	Large reasoning models are typically trained using reinforcement learning with verifiable reward (RLVR) positive and negative self-generated rollouts are used to update the model's policy . positive samples sharpen existing correct reasoning patterns, while negative samples encourage exploration of new reasoning paths.
Approach:	They propose a method that allocates advantage signals to key tokens across different polarities.
Outcome:	The proposed method improves the ability of large reasoning models to learn from their own generated rollouts.

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

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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.

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

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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

Multimodality

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.

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

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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

Lifting Optimized Binaries to Canonical Compiler IR via Structure-Aware Retrieval and Iterative Verification (2026.acl-long)

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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Xiaoao Zhu, Jie Ren, Zhiqiang Li, Jie Zheng, Zhanyong Tang, Zheng Wang

Information Retrieval

Challenge:	Existing methods for decompiling binary code are brittle due to compiler optimizations that distort control-flow and data-flow structure.
Approach:	They propose a system that lifts optimized binaries to canonical compiler intermediate representation (IR) BRIDGE uses control-flow-aware retrieval-augmented generation with feedback-driven verification .
Outcome:	The proposed system outperforms seven baselines on humanEval-Decompile and MBPP, lifting x86-64 and ARM64 binaries to LLVM IR.

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

Multimodality

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.

Are ChatGPT and GPT-4 General-Purpose Solvers for Financial Text Analytics? A Study on Several Typical Tasks (2023.emnlp-industry)

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Xianzhi Li, Samuel Chan, Xiaodan Zhu, Yulong Pei, Zhiqiang Ma, Xiaomo Liu, Sameena Shah

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

Challenge:	Recent large language models such as ChatGPT and GPT-4 have shown exceptional capabilities of generalist models . however, their applicability and effectiveness in specific domains like finance needs a better understanding .
Approach:	They conduct empirical studies to compare the performance of ChatGPT and GPT-4 on financial text analytical problems using eight benchmark datasets from five categories of tasks.
Outcome:	The proposed models outperform the state-of-the-art models on a wide range of financial text analytical tasks.

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

Fine-Tuning Language Models with Differential Privacy through Adaptive Noise Allocation (2024.findings-emnlp)

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.

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Xianzhi Li, Ran Zmigrod, Zhiqiang Ma, Xiaomo Liu, Xiaodan Zhu

SEAVER: Attention Reallocation for Mitigating Distractions in Language Models for Conditional Semantic Textual Similarity Measurement (2024.findings-emnlp)

Challenge:	Language models can memorize detailed information and patterns, but raise privacy concerns . ANADP reduces the performance gap between regular and DP fine-tuning while maintaining the privacy constraints.
Approach:	They propose an algorithm that allocates additive noise based on the importance of model parameters to reduce the performance gap between regular fine-tuning and traditional DP fine- tuning.
Outcome:	The proposed algorithm narrows the performance gap between regular fine-tuning and traditional DP fine- tuning while maintaining privacy constraints.

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Baixuan Li, Yunlong Fan, Zhiqiang Gao

LLMSurgeon: Diagnosing Data Mixture of Large Language Models (2026.acl-long)

Challenge:	Conditional Semantic Textual Similarity (C-STS) introduces specific limiting conditions to the traditional Semantics task.
Approach:	They propose a conditional semantic textual similarity (C-STS) task that introduces specific limiting conditions to the traditional Semantic Textual Similarity task.
Outcome:	The proposed model outperforms existing models on the C-STS-2023 test set and consistently improves on million-scale fine-tuning baseline models (up to 3 points).

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Yaxin Luo, Jiacheng Cui, Xiaohan Zhao, Xinyi Shang, Jiacheng Liu, Xinyue Bi, Zhaoyi Li, Zhiqiang Shen

Does RAG Introduce Unfairness in LLMs? Evaluating Fairness in Retrieval-Augmented Generation Systems (2025.coling-main)

Challenge:	a lack of transparency in large language models makes auditing their "digital DNA" difficult.
Approach:	They propose a framework that casts DMS as an inverse problem under label-shift assumption . they propose LLMScan, a recipe-verifiable evaluation suite built from open-source LLMs .
Outcome:	The proposed framework casts DMS as an inverse problem under label-shift assumption . compared with existing frameworks, it recovers domain mixtures with high fidelity .

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Xuyang Wu, Shuowei Li, Hsin-Tai Wu, Zhiqiang Tao, Yi Fang

SuperWriter: Reflection-Driven Long-Form Generation with Large Language Models (2026.findings-acl)

Challenge:	Retrieval-Augmented Generation (RAG) models address fairness concerns with respect to sensitive attributes such as gender, geographic location, and other demographic factors.
Approach:	They propose a framework to evaluate fairness in RAG using scenario-based questions and analyzing disparities across demographic attributes.
Outcome:	The proposed framework analyzes disparities across demographic attributes and identifies fairness issues in retrieval and generation stages.

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Yuhao Wu, Yushi Bai, Zhiqiang Hu, Juanzi Li, Roy Ka-Wei Lee

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

Challenge:	Long-form text generation remains a challenge for large language models . generating extended sequences often leads to degraded coherence and logical consistency .
Approach:	They propose a framework that integrates explicit structured thinking into long-form text generation.
Outcome:	The proposed framework surpasses even larger-scale models in evaluation and human evaluation.

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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.

Nested Browser-Use Learning for Agentic Information Seeking (2026.acl-long)

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Baixuan Li, Jialong Wu, Wenbiao Yin, Kuan Li, Zhongwang Zhang, Huifeng Yin, Zhengwei Tao, Liwen Zhang, Pengjun Xie, Jingren Zhou, Yong Jiang, Wentao Zhang, Zhiqiang Gao

Challenge:	Existing information-seeking (IS) agents rely on the web for their information acquisition.
Approach:	They propose a browser-action framework that decouples interaction control from page exploration through a nested structure.
Outcome:	Empirical results show that NestBrowse offers clear benefits in practice.

Judge and Improve: Towards a Better Reasoning of Knowledge Graphs with Large Language Models (2025.emnlp-main)

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Mo Zhiqiang, Yang Hua, Jiahui Li, Yuan Liu, Shawn Wong, Jianmin Huang

M-Ped: Multi-Prompt Ensemble Decoding for Large Language Models (2025.findings-emnlp)

Challenge:	Existing approaches to integrating graph and language models face two key limitations: achieving robust semantic alignment and ensuring interpretability in outputs.
Approach:	They propose a framework to integrate graph and language modalities while enhancing transparency.
Outcome:	Extensive experiments on three benchmark datasets show that the proposed framework surpasses existing methods in efficiency and generates outputs that are significantly more interpretable.

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

NOTA: Multimodal Music Notation Understanding for Visual Large Language Model (2025.findings-naacl)

Challenge:	a new ensemble decoding approach enhances the performance of Large Language Models.
Approach:	They propose a multi-prompt ensemble decoding approach to enhance LLM performance . they submit n variations of prompts with X to LLMs in batch mode to decode and derive probability distributions .
Outcome:	The proposed method improves pass@k rates, LENS metrics and BLEU scores on diverse NLP tasks.

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Mingni Tang, Jiajia Li, Lu Yang, Zhiqiang Zhang, Jinhao Tian, Zuchao Li, Lefei Zhang, Ping Wang

Multimodality

Challenge:	Existing general-domain visual language models lack ability of music notation understanding . Symbolic music is represented in two distinct forms: auditory music and symbolic music .
Approach:	They propose to train a multimodal music notation model using a large-scale dataset . they use cross-modal alignment to train the model for music notations analysis .
Outcome:	The proposed model improves on music understanding by training with a multimodal music notation model.

Optimal Expert-Attention Allocation in Mixture-of-Experts: A Scalable Law for Dynamic Model Design (2026.acl-industry)

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Junzhuo Li, Peijie Jiang, Changxin Tian, Jia Liu, Zhiqiang Zhang, Xuming Hu

Green & Sustainable NLP

Challenge:	a novel extension of neural scaling laws to Mixture-of-Experts models is proposed . a ratio of expert-attention compute is crucial for efficient MoE models .
Approach:	They propose an extension of neural scaling laws to Mixture-of-Experts (MoE) models . they define the ratio r as the fraction of total FLOPs per token dedicated to expert and attention layers .
Outcome:	The proposed model can be tuned beyond size and data with the proposed model.

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

Text Classification by Contrastive Learning and Cross-lingual Data Augmentation for Alzheimer’s Disease Detection (2020.coling-main)

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.

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Zhiqiang Guo, Zhaoci Liu, Zhenhua Ling, Shijin Wang, Lingjing Jin, Yunxia Li

Few-Shot Semantic Dependency Parsing via Graph Contrastive Learning (2024.lrec-main)

Challenge:	Existing methods for AD detection are too expensive and time-consuming to cover all potential patients.
Approach:	They propose a contrastive learning method to obtain effective text representations based on monolingual embeddings of BERT and a cross-lingual data augmentation method by building autoencoders to learn the text representation shared by both languages.
Outcome:	The proposed method outperforms other methods on a Mandarin AD corpus and achieves 81.6% detection accuracy.

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Bin Li, Yunlong Fan, Yikemaiti Sataer, Chuanqi Shi, Miao Gao, Zhiqiang Gao

RAPO: An Adaptive Ranking Paradigm for Bilingual Lexicon Induction (2022.emnlp-main)

Challenge:	Existing graph neural networks (GNNs) have shown promising performance on semantic dependency parsing (SDP) training a high-performing model requires a large amount of labeled data and it is prone to over-fitting in the absence of sufficient labele .
Approach:	They propose a syntax-guided graph contrastive learning framework to train GNNs with unlabeled data and fine-tune pre-trained GNN models with few-shot labeled SDP data.
Outcome:	The proposed framework achieves promising results when few-shot training samples are available.

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Zhoujin Tian, Chaozhuo Li, Shuo Ren, Zhiqiang Zuo, Zengxuan Wen, Xinyue Hu, Xiao Han, Haizhen Huang, Denvy Deng, Qi Zhang, Xing Xie

Multilinguality

Challenge:	Existing approaches focus on minimizing distances between words in aligned pairs, while suffering from low discriminative capability to distinguish the relative orders between positive and negative candidates.
Approach:	They propose a ranking-oriented induction model to learn personalized mapping function for each word.
Outcome:	The proposed model can learn personalized mapping function for each word on public datasets including rich-resource and low-resourced languages.

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

Enhancing Large Language Models for Document-Level Translation Post-Editing Using Monolingual Data (2025.coling-main)

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.

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Zongyao Li, Zhiqiang Rao, Hengchao Shang, Jiaxin Guo, Shaojun Li, Daimeng Wei, Hao Yang

Multilinguality

Challenge:	Large Language Models (LLMs) have excellent performance in many tasks, but they still face challenges in document translation.
Approach:	They propose a method that leverages the capabilities of Large Language Models to optimize document translation using only monolingual data.
Outcome:	The proposed method improves translation quality and improves contextual consistency in document translation using only monolingual data.

RASPberry: Retrieval-Augmented Monte Carlo Tree Self-Play with Reasoning Consistency for Multi-Hop Question Answering (2025.findings-acl)

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Baixuan Li, Yunlong Fan, Tianyi Ma, Miao Gao, Chuanqi Shi, Zhiqiang Gao

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

Challenge:	Existing methods for generating and analyzing multiple document knowledge are not effective for multi-hop question answering.
Approach:	They propose a Monte Carlo tree-based approach to inference-time scaling using RASPberry.
Outcome:	Experimental results show that the proposed method achieves better inference-time scaling on smaller LLMs.

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

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

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.

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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.

DynGL-SDP: Dynamic Graph Learning for Semantic Dependency Parsing (2022.coling-1)

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Bin Li, Miao Gao, Yunlong Fan, Yikemaiti Sataer, Zhiqiang Gao, Yaocheng Gui

LOG: A Local-to-Global Optimization Approach for Retrieval-based Explainable Multi-Hop Question Answering (2025.coling-main)

Challenge:	Existing parsers that learn graph representations based on static graphs are error-prone and disjointed . Graph-based parser can parse sentences efficiently but suffer from error propagation .
Approach:	They propose a dynamic graph learning framework to learn graph representations based on a static graph constructed by an existing parser.
Outcome:	The proposed parser outperforms the previous parsers on the SemEval-2015 task 18 dataset in three languages.

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Hao Xu, Yunxiao Zhao, Jiayang Zhang, Zhiqiang Wang, Ru Li

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

Challenge:	Existing approaches to multi-hop question answering emphasize single-step and multi-step iterative decomposition or retrieval, which are susceptible to failure in long-chain reasoning due to the progressive accumulation of erroneous information.
Approach:	They propose a Local-tO-Global optimized retrieval method to discover more beneficial information and improve tuplet objective loss.
Outcome:	The proposed method outperforms state-of-the-art models and significantly improves multi-hop reasoning.

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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%.

AGR: Reinforced Causal Agent-Guided Self-explaining Rationalization (2024.acl-short)

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Yunxiao Zhao, Zhiqiang Wang, Xiaoli Li, Jiye Liang, Ru Li