Papers by Jiacheng Li

An Empirical Study of Position Bias in Modern Information Retrieval (2025.findings-emnlp)

Challenge:	Existing tools to generate structured content for research tasks are limited in their ability to generate high-quality roadmaps.
Approach:	They propose a benchmark to evaluate the ability of large language models (LLMs) to generate high-quality roadmaps for solving complex research problems.
Outcome:	The proposed system can improve LLMs’ ability for roadmap generation while saving 84% of the time required by human experts.

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Ziyang Zeng, Dun Zhang, Jiacheng Li, null Zoupanxiang, Yudong Zhou, Yuqing Yang

Training Dynamics for Text Summarization Models (2022.findings-acl)

Challenge:	a new evaluation framework is used to assess the extent and impact of position bias in information retrieval.
Approach:	They introduce a position-aware retrieval benchmark and a diagnostic metric to quantify position bias . they compare models with BM25, dense embedding models, ColBERT-style late-interaction models .
Outcome:	The proposed framework evaluates retrieval models for position bias from a worst-case perspective.

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Tanya Goyal, Jiacheng Xu, Junyi Jessy Li, Greg Durrett

Information Extraction & Text Mining

Challenge:	Pre-trained language models have shown impressive results when fine-tuned on large summarization datasets.
Approach:	They analyze the training dynamics for generation models, focusing on summarization . they find that a propensity to copy the input is learned early in the training process .
Outcome:	The proposed model learns at different stages of fine-tuning, the authors show . they show that factual errors are learnt in later stages, but not at high-loss tokens .

TritonBench: Benchmarking Large Language Model Capabilities for Generating Triton Operators (2025.findings-acl)

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Jianling Li, ShangZhan Li, Zhenye Gao, Qi Shi, Yuxuan Li, Zefan Wang, Jiacheng Huang, WangHaojie WangHaojie, Jianrong Wang, Xu Han, Zhiyuan Liu, Maosong Sun

Open-world Semi-supervised Generalized Relation Discovery Aligned in a Real-world Setting (2023.emnlp-main)

Challenge:	Triton is a high-level Python-like programming language for building efficient GPU kernels.
Approach:	They propose a TritonBench benchmark that provides a comprehensive evaluation of Tritonic operators on widely deployed GPUs.
Outcome:	The proposed benchmarks show that current LLMs struggle to generate efficient Triton operators on widely deployed GPUs aligned with industry applications.

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William Hogan, Jiacheng Li, Jingbo Shang

Low-Resource NLP

Challenge:	Existing approaches to open-world relation extraction assume that all instances of unlabeled data belong to novel classes.
Approach:	They propose a method that classifies relations from known and novel classes within unlabeled data.
Outcome:	The proposed method outperforms existing methods on Open-world RE benchmarks.

BTC-LLM: Efficient Sub-1-Bit LLM Quantization via Learnable Transformation and Binary Codebook (2026.acl-long)

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Hao Gu, Lujun Li, Hao Wang, Lei Wang, Zheyu Wang, Bei Liu, Jiacheng Liu, Qiyuan Zhu, Sirui Han, Yike Guo

Text Clustering

Challenge:	Recent sparsity-aware binarization approaches can achieve sub-1-bit compression, but they face performance degradation, mask-management overhead, and limited hardware compatibility.
Approach:	They propose a binary quantization framework that leverages binary pattern clustering and weight transformation to overcome performance degradation and mask-management overhead.
Outcome:	The proposed framework achieves state-of-the-art compression (1.11–0.7 bits) it maintains high performance with only a 3.1% accuracy drop in zero-shot benchmarks while delivering a 1.6 speedup over FP16.

SeNsER: Learning Cross-Building Sensor Metadata Tagger (2020.findings-emnlp)

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Yang Jiao, Jiacheng Li, Jiaman Wu, Dezhi Hong, Rajesh Gupta, Jingbo Shang

Challenge:	Sensor metadata tagging is a key component of smart building applications.
Approach:	They propose a framework that learns a sensor metadata tagger for a new building based on its raw metadata and some existing fully annotated building.
Outcome:	The proposed framework learns a sensor metadata tagger for a new building based on its raw metadata and some existing fully annotated building.

Bridging Language and Items for Retrieval and Recommendation: Benchmarking LLMs as Semantic Encoders (2026.acl-long)

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Yupeng Hou, Jiacheng Li, Xiangjun Fu, Zhankui He, An Yan, Xiusi Chen, Julian McAuley

FinanceReasoning: Benchmarking Financial Numerical Reasoning More Credible, Comprehensive and Challenging (2025.acl-long)

Challenge:	Recent advances in large language models have enabled their use as semantic encoders for recommendation, but their roles and behaviors in this setting are still not well understood.
Approach:	They propose a benchmark to evaluate large language models as semantic encoders in recommendation scenarios.
Outcome:	The proposed benchmark shows that ranking of 11 leading LLMs is low compared to MTEB, highlighting the unique challenges of semantic encoding in recommendation.

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Zichen Tang, Haihong E, Ziyan Ma, Haoyang He, Jiacheng Liu, Zhongjun Yang, Zihua Rong, Rongjin Li, Kun Ji, Qing Huang, Xinyang Hu, Yang Liu, Qianhe Zheng

Reasoning

Challenge:	Compared to existing benchmarks, FinanceReasoning provides three key advancements: (1) credibility; (2) comprehensiveness; (3) numerical precision; (4) complexity; (5) complexity; and (6) complexity.
Approach:	They propose a benchmark to evaluate the reasoning capabilities of large reasoning models (LRMs) in financial numerical reasoning problems.
Outcome:	The proposed benchmark exceeds existing benchmarks in 67.8% of financial concepts and formulas and is credible, comprehensive, and challenging.

STORM-BORN: A Challenging Mathematical Derivations Dataset Curated via a Human-in-the-Loop Multi-Agent Framework (2025.findings-acl)

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Wenhao Liu, Zhenyi Lu, Xinyu Hu, Jerry Zhang, Dailin Li, Jiacheng Cen, Huilin Cao, Haiteng Wang, Yuhan Li, Xie Kun, Dandan Li, Pei Zhang, Chengbo Zhang, Yuxiang Ren, Xiaohong Huang, Yan Ma

Reasoning

Challenge:	Existing datasets suffer from outdated and insufficient challenging content, neglecting human-like reasoning, and limited reliability due to single-LLM generation.
Approach:	They propose a human-in-the-loop, multi-agent data generation framework that integrates reasoning-dense filters, multiagent collaboration, and human mathematicians’ evaluations to ensure the reliability and quality of the dataset.
Outcome:	The proposed framework improves accuracy and quality of the 2,000-synthesized datasets by integrating reasoning-dense filters, multi-agent collaboration, and human mathematicians’ evaluations.

Bit-by-Bit: Progressive QAT Strategy with Outlier Channel Splitting for Stable Low-Bit LLMs (2026.acl-long)

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Binxing Xu, Hao Gu, Lujun Li, Hao Wang, Bei Liu, Jiacheng Liu, Qiyuan Zhu, Xintong Yang, Chao Li, Sirui Han, Yike Guo

Green & Sustainable NLP

Challenge:	Existing approaches to training LLMs at ultra-low precisions suffer from convergence instability and substantial training costs.
Approach:	They propose a progressive QAT framework with outlier channel splitting to address these issues . they use nested structure of integer quantization grids to enable a "train once, deploy any precision" paradigm .
Outcome:	The proposed framework outperforms baselines on both Llama2/3 and W2A16, with an 11 speedup over BF16.

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

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

SCCS: Semantics-Consistent Cross-domain Summarization via Optimal Transport Alignment (2023.findings-acl)

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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Jielin Qiu, Jiacheng Zhu, Mengdi Xu, Franck Dernoncourt, Trung Bui, Zhaowen Wang, Bo Li, Ding Zhao, Hailin Jin

Understanding the Language Model to Solve the Symbolic Multi-Step Reasoning Problem from the Perspective of Buffer Mechanism (2025.findings-emnlp)

Challenge:	Existing methods for multimodal summarization ignore the structure and semantics of the whole video and article.
Approach:	They propose a semantic-consistent cross-domain summarization model that extracts features from video and article and uses fusion methods to select representative one.
Outcome:	The proposed model produces high-quality multimodal summaries on three MSMO datasets.

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Zhiwei Wang, Yunji Wang, Zhongwang Zhang, Zhangchen Zhou, Hui Jin, Tianyang Hu, Jiacheng Sun, Zhenguo Li, Yaoyu Zhang, Zhi-Qin John Xu

Data Augmentation for Text Generation Without Any Augmented Data (2021.acl-long)

Challenge:	Large language models struggle with complex reasoning tasks, such as mathematical problem-solving.
Approach:	They constructed a symbolic multi-step reasoning task to investigate the information propagation mechanisms in Transformer models when solving the task through direct answering and Chain-of-Thought (CoT) reasoning.
Outcome:	The proposed algorithm improves on 7 multi-step reasoning datasets, while introducing only 132 trainable parameters.

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Wei Bi, Huayang Li, Jiacheng Huang

How Do LLMs Acquire New Knowledge? A Knowledge Circuits Perspective on Continual Pre-Training (2025.findings-acl)

Challenge:	Existing methods for data augmentation need to define or choose proper data mapping functions to create augmented samples.
Approach:	They propose to use data mapping functions to augment text samples without using specific mapping functions.
Outcome:	The proposed approach can approximate or even surpass popular data augmentation methods on two text generation tasks with a convergence rate guarantee.

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Yixin Ou, Yunzhi Yao, Ningyu Zhang, Hui Jin, Jiacheng Sun, Shumin Deng, Zhenguo Li, Huajun Chen

MuCGEC: a Multi-Reference Multi-Source Evaluation Dataset for Chinese Grammatical Error Correction (2022.naacl-main)

Challenge:	Large Language Models (LLMs) have exceptional capabilities in knowledge-intensive tasks . however, they struggle with knowledge updates due to dynamic nature of world knowledge .
Approach:	They propose to identify computational subgraphs that facilitate knowledge storage and processing . they also identify a phase shift from formation to optimization in LLMs .
Outcome:	The proposed model can capture factual knowledge from pre-training corpus and encapsulate it as extensive parametric knowledge.

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Yue Zhang, Zhenghua Li, Zuyi Bao, Jiacheng Li, Bo Zhang, Chen Li, Fei Huang, Min Zhang

Syntactic Text Processing

Challenge:	Using a multi-reference multi-source evaluation dataset, Chinese grammatical error correction (CGEC) is relatively scarce.
Approach:	They propose a multi-reference multi-source evaluation dataset for Chinese grammar error correction . the dataset contains 7,063 sentences written by Chinese-as-a-Second-Language learners .
Outcome:	The proposed dataset can be used to evaluate Chinese grammar errors in Chinese.

Text-to-Table: A New Way of Information Extraction (2022.acl-long)

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Xueqing Wu, Jiacheng Zhang, Hang Li

Beyond Benchmarks: A Capability-Based Maturity Model for Systematic AI Integration in Hospitals (2026.findings-acl)

Challenge:	Existing methods for information extraction are not well understood . text-to-table is a problem that aims to extract information from text data .
Approach:	They propose a new problem setting of information extraction, called text-to-table . they formalize text- to-table as a sequence-tosequence problem .
Outcome:	The proposed method outperforms existing methods on text-to-table tasks.

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Rui Li, Xiaofen WU, Mingqian Liu, Xiaoxia Song, Xu Xiangjun, Jiacheng Qiao, Zhuang Boqin, Xu Chen

Challenge:	Current Large Language Models (LLMs) excel in standardized tests focused on medical knowledge recall, but not in real-world healthcare scenarios.
Approach:	They propose a "capability-based hospital AI Maturity Model" framework that categorizes capabilities into distinct maturity levels . medical artificial intelligence is currently at a critical transition stage from technical verification to deep clinical integration .
Outcome:	The proposed model provides a clear, stepwise evolutionary path for hospitals from foundational infrastructure construction to ubiquitous intelligence.

QaRL: Rollout-Aligned Quantization-Aware RL for Fast and Stable Training under Training–Inference Mismatch (2026.findings-acl)

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Hao Gu, Hao Wang, Jiacheng Liu, Lujun Li, Qiyuan Zhu, Bei Liu, Binxing Xu, Lei Wang, Xintong Yang, Sida Lin, Sirui Han, Yike Guo

Challenge:	Recent work has shown that reinforcement learning with simple rule-based reward functions (RLVR) can induce emergent reasoning behaviors and yield gains in challenging domains such as math problem solving.
Approach:	They propose a rollout-alignment-quantization-aware RL which aligns training-side forward with the quantized rollout to minimize mismatch.
Outcome:	The proposed approach outperforms quantized-rollout training by +5.5 on Qwen3-30B-A3B MoE for math problems while maintaining low-bit throughput.

ZeroGen: Efficient Zero-shot Learning via Dataset Generation (2022.emnlp-main)

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Jiacheng Ye, Jiahui Gao, Qintong Li, Hang Xu, Jiangtao Feng, Zhiyong Wu, Tao Yu, Lingpeng Kong

WISCA: A Lightweight Model Transition Method to Improve LLM Training via Weight Scaling (2026.findings-acl)

Challenge:	Existing approaches to generate training data with pre-trained language models have been found effective in various scenarios.
Approach:	They propose an unsupervised zero-shot learning method that generates a dataset from scratch and trains a tiny task model under supervision of the synthesized dataset.
Outcome:	The proposed method is annotated-free and efficient, but can provide useful insights from the perspective of data-free model-agnostic knowledge distillation and unreferenced text generation evaluation.

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Jiacheng Li, Jianchao Tan, Zhidong Yang, Pingwei Sun, Feiye Huo, Jiayu Qin, Xiangyu Zhang, Maoxin He, Guangming Tan, Weile Jia, Xunliang Cai, Tong Zhao

Generating Data for Symbolic Language with Large Language Models (2023.emnlp-main)

Challenge:	Recent advances in training optimization for Transformer-based large language models lack systematic optimization of weight patterns during training.
Approach:	They propose a Weight Scaling method that rescales weights while preserving model outputs to improve model training efficiency and model quality.
Outcome:	The proposed method significantly improves convergence quality and loss reduction in LLMs with Grouped Query Attention architectures and LoRA fine-tuning tasks.

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Jiacheng Ye, Chengzu Li, Lingpeng Kong, Tao Yu

AEGIS: A Holistic Benchmark for Evaluating Forensic Analysis of AI-Generated Academic Images (2026.acl-long)

Challenge:	Large language models (LLMs) bring performance and complexity, but they incur a large computational cost in practice.
Approach:	They propose a task-based model which uses large language models to generate symbolic language data by an informative prompt and agreement-based verifier.
Outcome:	The proposed model can generate symbolic language data with a few human demonstrations and saves a considerable amount of inference effort.

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Bo Zhang, Tzu-Yen Ma, Zichen Tang, Junpeng Ding, Zirui Wang, Yizhuo Zhao, Peilin Gao, Zijie Xi, Zixin Ding, Haiyang Sun, Haocheng Gao, Yuan Liu, Liangjia Wang, Yiling Huang, Yujie Wang, Yuyue Zhang, Ronghui Xi, Yuanze Li, Jiacheng Liu, Zhongjun Yang, Haihong E

UCTopic: Unsupervised Contrastive Learning for Phrase Representations and Topic Mining (2022.acl-long)

Challenge:	AEGIS examines whether current models can effectively audit AI-generated images in academic papers.
Approach:	They propose a holistic benchmark for forensic analysis of AI-Generated academic ImageS that reveals limitations in academic image forensics.
Outcome:	AEGIS compared with existing benchmarks on seven academic categories and features key advances in forensic analysis.

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Jiacheng Li, Jingbo Shang, Julian McAuley

Decoding Scientific Experimental Images: The SPUR Benchmark for Perception, Understanding, and Reasoning (2026.acl-long)

Challenge:	Existing phrase representation learning methods combine unigram representations in a context-free manner or rely on extensive annotations to learn context-aware knowledge.
Approach:	They propose a novel unsupervised contrastive learning framework for context-aware phrase representations and topic mining.
Outcome:	The proposed framework outperforms the state-of-the-art phrase representation model by 38.2% NMI on four entity clustering tasks.

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Junpeng Ding, Zichen Tang, Haihong E, Mengyuan Ji, Yang Liu, Haolin Tian, Haiyang Sun, Pengqi Sun, Yang Xu, Yichen Liu, Haocheng Gao, Zijie Xi, Ruomeng Jiang, Peizhi Zhao, Rongjin Li, Yuanze Li, Jiacheng Liu, Zhongjun Yang, Jintong Chen, Siying Lin

LoRAExit: Empowering Dynamic Modulation of LLMs in Resource-limited Settings using Low-rank Adapters (2024.findings-emnlp)

Challenge:	Xu and Peng, 2025) . . SPUR is a comprehensive benchmark for scientific experimental image perception, understanding, and reasoning, comprising 4,264 question-answering (QA) pairs derived from 1,084 expert-curated images.
Approach:	They propose to use 4,264 question-answering (QA) pairs derived from 1,084 expert-curated images to evaluate the visual perception of multimodal large language models (MLLMs) . they also propose to utilize cross-panel relation understanding to evaluate MLLM’s ability to decipher intricate cross-panel relations.
Outcome:	The proposed model is based on 4,264 question-answering pairs derived from 1,084 expert-curated images.

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Jiacheng Liu, Peng Tang, Xiaofeng Hou, Chao Li, Pheng-Ann Heng

Weakly Supervised Named Entity Tagging with Learnable Logical Rules (2021.acl-long)

Challenge:	Large Language Models (LLMs) have exhibited remarkable performance across various natural language processing tasks, but deployment on resource-limited settings remains a challenge.
Approach:	They propose a dynamic inference architecture that leverages low-rank adaptors for efficient deployment of LLMs.
Outcome:	The proposed architecture significantly improves performance when deployed on resource-limited settings.

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Jiacheng Li, Haibo Ding, Jingbo Shang, Julian McAuley, Zhe Feng

Fine-grained Contrastive Learning for Relation Extraction (2022.emnlp-main)

Challenge:	Existing methods for building entity tagging systems use weak supervision . previous methods focus on disambiguating entity types based on contexts and expert-provided rules .
Approach:	They propose a method that bootstraps high-quality logical rules to train a neural tagger in a fully automated manner.
Outcome:	The proposed method outperforms weakly supervised methods on three datasets . it rivals state-of-the-art supervised method with lexicon of over 2,000 terms .

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William Hogan, Jiacheng Li, Jingbo Shang

Justifying Recommendations using Distantly-Labeled Reviews and Fine-Grained Aspects (D19-1)

Challenge:	Existing methods assume all silver labels are accurate and treat them equally, but distant supervision is noisy–some silver labels more reliable than others.
Approach:	They propose a noise-aware contrastive learning approach that leverages fine-grained information about which silver labels are and are not noisy to improve the quality of learned relationship representations.
Outcome:	The proposed approach improves relation extraction performance over state-of-the-art methods on several RE benchmarks.

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Jianmo Ni, Jiacheng Li, Julian McAuley

Challenge:	Existing approaches to generating reviews struggle to generate justifications that are relevant to users’ decision-making process.
Approach:	They propose an ‘extractive’ approach to identify review segments which justify users’ intentions and use it to distantly label massive review corpora and construct large-scale personalized recommendation justification datasets.
Outcome:	The proposed model can generate convincing and diverse justifications from massive review corpora and distantly label massive review data.

An Empirical Study of LLM Reasoning Ability Under Strict Output Length Constraint (2025.emnlp-main)

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Yi Sun, Han Wang, Jiaqiang Li, Jiacheng Liu, Xiangyu Li, Hao Wen, Yizhen Yuan, Huiwen Zheng, Yan Liang, Yuanchun Li, Yunxin Liu

Low-Resource NLP

Challenge:	Large Language Models (LLMs) are a powerful tool for test-time scaling, but they are often used under time constraints.
Approach:	They propose to use LLMs to make models think before answering questions . they also use self-correction and best-of-N decoding to encourage deeper thinking .
Outcome:	The proposed models are able to achieve higher inference accuracy with extra inference computation under time constraints.

Uncertainty-Aware Label Refinement for Sequence Labeling (2020.emnlp-main)

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Tao Gui, Jiacheng Ye, Qi Zhang, Zhengyan Li, Zichu Fei, Yeyun Gong, Xuanjing Huang

Green & Sustainable NLP

Challenge:	Conditional random fields (CRF) for label decoding have been a problem for many tasks.
Approach:	They propose a two-stage label decoding framework that model long-term label dependencies while being much more computationally efficient.
Outcome:	The proposed method outperforms the CRF-based methods and greatly accelerates the inference process.

TextFlint: Unified Multilingual Robustness Evaluation Toolkit for Natural Language Processing (2021.acl-demo)

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Xiao Wang, Qin Liu, Tao Gui, Qi Zhang, Yicheng Zou, Xin Zhou, Jiacheng Ye, Yongxin Zhang, Rui Zheng, Zexiong Pang, Qinzhuo Wu, Zhengyan Li, Chong Zhang, Ruotian Ma, Zichu Fei, Ruijian Cai, Jun Zhao, Xingwu Hu, Zhiheng Yan, Yiding Tan, Yuan Hu, Qiyuan Bian, Zhihua Liu, Shan Qin, Bolin Zhu, Xiaoyu Xing, Jinlan Fu, Yue Zhang, Minlong Peng, Xiaoqing Zheng, Yaqian Zhou, Zhongyu Wei, Xipeng Qiu, Xuanjing Huang

Robustness in NLP

Challenge:	Existing approaches to textual robustness evaluation focus on slightly modifying the input data, which maintains the original meaning and results in a different prediction.
Approach:	They propose a multilingual robustness evaluation toolkit for NLP that integrates universal text transformations, task-specific transformations and adversarial attack.
Outcome:	The toolkit includes universal text transformation, task-specific transformation, adversarial attack, subpopulation, and their combinations to provide comprehensive robustness analyses.

Can Brain Signals Reveal Inner Alignment with Human Languages? (2023.findings-emnlp)

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Jielin Qiu, William Han, Jiacheng Zhu, Mengdi Xu, Douglas Weber, Bo Li, Ding Zhao

Sentiment Analysis

Challenge:	Brain Signals, such as Electroencephalography, and human languages have been explored independently for many downstream tasks, however, the connection between them has not been well explored.
Approach:	They introduce a multimodal transformer alignment model to observe coordinated representations between EEG and language.
Outcome:	The proposed method achieved an F1-score improvement of 1.7% on ZuCo and 9.3% on Zuco datasets for sentiment analysis, and 7.4% on ZuCO for relation detection.

Can LLMs See Without Pixels? Benchmarking Spatial Intelligence from Textual Descriptions (2026.findings-acl)

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Zhongbin Guo, Zhen Yang, Yushan Li, Xinyue Zhang, Wenyu Gao, Jiacheng Wang, Chengzhi Li, Xiangrui Liu, Ping Jian

How Numerical Precision Affects Arithmetical Reasoning Capabilities of LLMs (2025.findings-acl)

Challenge:	Existing advances in Spatial Intelligence rely on vision-Language Models . however, a critical question remains: does spatial understanding originate from visual encoders?
Approach:	They propose to evaluate the SI performance of Large Language Models without pixel-level input.
Outcome:	The proposed benchmark challenges large language models to perform symbolic reasoning rather than visual pattern matching.

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Guhao Feng, Kai Yang, Yuntian Gu, Xinyue Ai, Shengjie Luo, Jiacheng Sun, Di He, Zhenguo Li, Liwei Wang

Reasoning

Challenge:	Despite the success of transformer-based large language models, understanding and enhancing their mathematical capabilities remains a significant challenge.
Approach:	They propose to use numerical precision as a key factor that influences LLMs' effectiveness in arithmetical tasks to determine their effectiveness.
Outcome:	The proposed models perform better in arithmetic tasks than transformer-based models with standard numerical precision.

Enhancing Agentic Textual Graph Retrieval with Synthetic Stepwise Supervision (2026.acl-long)

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Ge Chang, Jinbo Su, Jiacheng Liu, Pengfei Yang, Yuhao Shang, Huiwen Zheng, Hongli Ma, Yan Liang, Yuanchun Li, Yunxin Liu