Papers by Cheng Luo

Cross-View Language Modeling: Towards Unified Cross-Lingual Cross-Modal Pre-training (2023.acl-long)

Challenge:	Existing studies have shown that adapter-based tuning is more parameter-efficient than fine-tuning.
Approach:	They propose to add adapter modules to a pretrained language model and update the parameters of adapter module when learning on a downstream task.
Outcome:	The proposed method outperforms fine-tuning on low-resource and cross-lingual tasks and settings.

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Yan Zeng, Wangchunshu Zhou, Ao Luo, Ziming Cheng, Xinsong Zhang

Open-Ended Visual Question Answering by Multi-Modal Domain Adaptation (2020.findings-emnlp)

Challenge:	Empirical results show that CCLM significantly outperforms the prior state-of-the-art with an average absolute improvement of over 10%.
Approach:	They introduce a pre-training framework that unifies cross-lingual and cross-modal pre-trained models with shared architectures and objectives.
Outcome:	The proposed framework outperforms the state-of-the-art in two multi-lingual datasets and two multilingual image-text retrieval datasets.

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Yiming Xu, Lin Chen, Zhongwei Cheng, Lixin Duan, Jiebo Luo

Disentangling Memory and Reasoning Ability in Large Language Models (2025.acl-long)

Challenge:	Existing approaches to visual question answering (VQA) are not suitable for real-world applications.
Approach:	They propose a supervised multi-modal domain adaptation method for visual question answering in images that exploits supervised domain adaptation.
Outcome:	The proposed method outperforms state-of-the-art methods on the benchmark VQA 2.0 and VizWiz datasets.

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Mingyu Jin, Weidi Luo, Sitao Cheng, Xinyi Wang, Wenyue Hua, Ruixiang Tang, William Yang Wang, Yongfeng Zhang

Velocitune: A Velocity-based Dynamic Domain Reweighting Method for Continual Pre-training (2025.acl-long)

Challenge:	Existing LLMs operate as an opaque process without explicit separation between knowledge retrieval and reasoning steps, making the decision-making process unclear and disorganized.
Approach:	They propose a language model inference paradigm that decomposes the complex inference process into two distinct and clear actions: (1) memory recall: which retrieves relevant knowledge, and (2) reasoning: which performs reasoning steps based on the recalled knowledge.
Outcome:	The proposed paradigm decomposes the inference process into two distinct and clear actions, memory and reason, guiding the model to distinguish between steps that require knowledge retrieval and those that involve reasoning.

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Zheheng Luo, Xin Zhang, Xiao Liu, Haoling Li, Yeyun Gong, Qi Chen, Peng Cheng

Simple but Challenging: Natural Language Inference Models Fail on Simple Sentences (2022.findings-emnlp)

Challenge:	Existing methods to optimise pretraining performance have not addressed the complexities of domain-adaptive continual pretraining.
Approach:	They propose a framework that dynamically assesses learning velocity and adjusts data proportions accordingly, favouring slower learning domains while de-emphasising faster learning ones.
Outcome:	The proposed framework achieves performance gains in math and code reasoning tasks and command-line generation benchmarks.

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Cheng Luo, Wei Liu, Jieyu Lin, Jiajie Zou, Ming Xiang, Nai Ding

CofiPara: A Coarse-to-fine Paradigm for Multimodal Sarcasm Target Identification with Large Multimodal Models (2024.acl-long)

Challenge:	Natural language inference (NLI) tasks are difficult to perform on large datasets . a small number of simple sentences can improve model performance, authors say .
Approach:	They propose to use syntactically simple sentences to test the inference ability of NLI models.
Outcome:	The proposed set of simple sentences shows that the models fine-tuned on MNLI and SNLI perform poorly on Simple Pair.

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Zixin Chen, Hongzhan Lin, Ziyang Luo, Mingfei Cheng, Jing Ma, Guang Chen

Sentiment Analysis

Challenge:	Current methods for multimodal sarcasm target identification focus on superficial indicators in an end-to-end manner, overlooking the nuanced understanding of multimodal content.
Approach:	They propose a multimodal sarcasm target identification framework with a coarse-to-fine paradigm by augmenting sarcasm explainability with reasoning and pre-training knowledge.
Outcome:	The proposed framework outperforms state-of-the-art methods and exhibits explainability in deciphering sarcasm as well.

BlendFilter: Advancing Retrieval-Augmented Large Language Models via Query Generation Blending and Knowledge Filtering (2024.emnlp-main)

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Haoyu Wang, Ruirui Li, Haoming Jiang, Jinjin Tian, Zhengyang Wang, Chen Luo, Xianfeng Tang, Monica Cheng, Tuo Zhao, Jing Gao

Data Interpreter: An LLM Agent for Data Science (2025.findings-acl)

Challenge:	Retrieval-augmented Large Language Models struggle with complex inputs and noisy knowledge retrieval hindering model effectiveness.
Approach:	They propose a query generation method that integrates query generation blending with knowledge filtering to enhance retrieval-augmented LLMs.
Outcome:	The proposed approach surpasses state-of-the-art benchmarks on open-domain question answering benchmarks.

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Sirui Hong, Yizhang Lin, Bang Liu, Bangbang Liu, Binhao Wu, Ceyao Zhang, Danyang Li, Jiaqi Chen, Jiayi Zhang, Jinlin Wang, Li Zhang, Lingyao Zhang, Min Yang, Mingchen Zhuge, Taicheng Guo, Tuo Zhou, Wei Tao, Robert Tang, Xiangtao Lu, Xiawu Zheng, Xinbing Liang, Yaying Fei, Yuheng Cheng, Yongxin Ni, Zhibin Gou, Zongze Xu, Yuyu Luo, Chenglin Wu

Responsible & Trustworthy NLP

Challenge:	Large Language Models (LLMs) excel in various domains but face challenges when applied to data science workflows due to their complex, multi-stage nature.
Approach:	They propose a hierarchical graph-based agent that represents complexity and a progressive strategy for step-by-step verification, refinement, and consistent context management.
Outcome:	The proposed agent surpasses state-of-the-art baselines on the MATH dataset and performs better on InfiAgent-DABench.

Multi-level Distillation of Semantic Knowledge for Pre-training Multilingual Language Model (2022.emnlp-main)

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Mingqi Li, Fei Ding, Dan Zhang, Long Cheng, Hongxin Hu, Feng Luo

Causality-Guided Multi-Memory Interaction Network for Multivariate Stock Price Movement Prediction (2023.acl-long)

Challenge:	Existing methods for improving multilingual models did not focus on learning the semantic structure of representation.
Approach:	They propose a method to improve multilingual language models by aligning parallel sentences . they propose token-, word-, sentence- and structure-level alignment objectives .
Outcome:	The proposed method outperforms baseline models on XNLI, PAWS-X, and XQuAD . it obtains comparable performance on low-resource languages, the authors show .

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Di Luo, Weiheng Liao, Shuqi Li, Xin Cheng, Rui Yan

MReD: A Meta-Review Dataset for Structure-Controllable Text Generation (2022.findings-acl)

Challenge:	Existing models for stock price movement prediction use auxiliary data, but we assume other stocks should be utilized as auxiliary information to enhance performance.
Approach:	They propose a Causality-guided multi-memory interaction network for stock movement prediction which transforms basic attention into Causal Attention by calculating transfer entropy between multivariate stocks.
Outcome:	The proposed model outperforms existing models on three real-world datasets from the U.S. and Chinese markets.

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Chenhui Shen, Liying Cheng, Ran Zhou, Lidong Bing, Yang You, Luo Si

Text Generation

Challenge:	a new text generation dataset is needed to controllable text summarization, but it lacks the domain knowledge.
Approach:	They propose to use existing text generation datasets to leverage input and control signals . they propose to annotate each meta-review sentence manually with a control signal .
Outcome:	The proposed method can be used to control the structure of a text generation dataset . it can be applied to a variety of tasks, including a task with a large number of meta-review sentences .

Argument Pair Extraction via Attention-guided Multi-Layer Multi-Cross Encoding (2021.acl-long)

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Liying Cheng, Tianyu Wu, Lidong Bing, Luo Si

Document Segmentation Matters for Retrieval-Augmented Generation (2025.findings-acl)

Challenge:	Argument pair extraction (APE) is a research task for extracting arguments from two passages and identifying potential argument pairs.
Approach:	They propose a novel attention-guided multi-layer multi-cross encoding scheme that processes two passages with two individual sequence encoders and updates their representations using each other’s attention.
Outcome:	The proposed model significantly improves the performance over several alternatives.

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Zhitong Wang, Cheng Gao, Chaojun Xiao, Yufei Huang, Shuzheng Si, Kangyang Luo, Yuzhuo Bai, Wenhao Li, Tangjian Duan, Chuancheng Lv, Guoshan Lu, Gang Chen, Fanchao Qi, Maosong Sun

Syntactic Text Processing

Challenge:	Existing rule-based chunking methods lead to suboptimal splits, where overly large chunks introduce irrelevant information and small chunks lack semantic coherence.
Approach:	They propose a method that leverages document summaries as pseudo-instructions to guide chunking by computing semantic similarity between sentences and the summary.
Outcome:	Experiments on multiple open-domain question-answering benchmarks show that PIC significantly improves retrieval accuracy (Hits@k) and end-to-end QA performance (Exact Match) without any additional training.

PEPE: Long-context Extension for Large Language Models via Periodic Extrapolation Positional Encodings (2025.findings-emnlp)

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Jikun Hu, Dongsheng Guo, Yuli Liu, Qingyao Ai, Lixuan Wang, Xuebing Sun, Qilei Zhang, Quan Zhou, Cheng Luo

MedQPA-Gen: Medical Question Proposing and Answering for Report Generation (2026.findings-acl)

Challenge:	Long-context extension attempts to extend contextual window in pre-trained LLMs . primary method involves expanding initial positional encodings, disrupting positional learning .
Approach:	They propose a new extension strategy based on Rotary Position Embedding to extend contextual window in pre-trained large language models.
Outcome:	The proposed method can extend the contextual window in pre-trained large language models . expansion disrupts positional encodings learned during pre-training, authors show .

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Weijie Liang, Xiyue Zhu, Ruike Zhu, Chenhao Li, Cheng Tang, Zhiyu Liu, Zhihua Gong, Shirui Luo, Yudu Li, Volodymyr Kindratenko

Natural Language Interfaces

Challenge:	Existing training paradigms fail to explicitly target factual accuracy, resulting in inaccuracies and serious patient safety risks.
Approach:	They propose an automatic and generalizable report evaluation technique that uses question proposing and answering to enable controllable, structured reasoning grounded in medical domain knowledge and the factual correctness of the report.
Outcome:	The proposed method can improve human preference scores and perform better on downstream tasks.

EscapeBench: Towards Advancing Creative Intelligence of Language Model Agents (2025.acl-long)

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Cheng Qian, Peixuan Han, Qinyu Luo, Bingxiang He, Xiusi Chen, Yuji Zhang, Hongyi Du, Jiarui Yao, Xiaocheng Yang, Denghui Zhang, Yunzhu Li, Heng Ji

JarviX: A LLM No code Platform for Tabular Data Analysis and Optimization (2023.emnlp-industry)

Challenge:	Existing language model agents excel in planning and reasoning, but lack creativity in unfamiliar environments.
Approach:	They propose a benchmark suite of room escape game environments to challenge agents with creative reasoning, unconventional tool use and iterative problem-solving to uncover implicit goals.
Outcome:	The proposed framework can perform with 40% fewer steps and hints and performs robustly across difficulty levels.

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Shang-Ching Liu, ShengKun Wang, Tsungyao Chang, Wenqi Lin, Chung-Wei Hsiung, Yi-Chen Hsieh, Yu-Ping Cheng, Sian-Hong Luo, Jianwei Zhang

MEIC-DT: Memory-Efficient Incremental Clustering for Long-Text Coreference Resolution with Dual-Threshold Constraints (2026.findings-acl)

Challenge:	Tabular data analysis is an important application task of large language models, but advanced models are not yet on par with expert level performance.
Approach:	They propose to employ Large Language Models to facilitate an automated guide and execute high-precision data analyzes on tabular datasets.
Outcome:	The proposed framework is based on large language models and an automated machine learning pipeline for predictive modeling.

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Kangyang Luo, Shuzheng Si, Yuzhuo Bai, Cheng Gao, Zhitong Wang, Cheng Huang, Yingli Shen, Yufeng Han, Wenhao Li, Cunliang Kong, Maosong Sun

Original Content Is All You Need! an Empirical Study on Leveraging Answer Summary for WikiHowQA Answer Selection Task (2022.coling-1)

Challenge:	Existing supervised neural methods are underexplored for coreference resolution, especially in incremental clustering.
Approach:	They propose a dual-threshold incremental clustering approach based on a lightweight Transformer.
Outcome:	Experiments on common benchmarks show that MEIC-DT achieves highly competitive coreference performance under stringent memory constraints.

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Liang Wen, Juan Li, Houfeng Wang, Yingwei Luo, Xiaolin Wang, Xiaodong Zhang, Zhicong Cheng, Dawei Yin

CodeTool: Enhancing Programmatic Tool Invocation of LLMs via Process Supervision (2025.acl-long)

Challenge:	Existing answer selection approaches for community question answering lack additional answer summaries due to redundancy and lengthiness issues of crowdsourced answers.
Approach:	They constructed a dataset which contains a corresponding reference summary for each original lengthy answer.
Outcome:	The proposed model improves the performance of a question and candidate answer on a WikiHowQA dataset.

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YifeiLu YifeiLu, Fanghua Ye, Jian Li, Qiang Gao, Cheng Liu, Haibo Luo, Nan Du, Xiaolong Li, Feiliang Ren

Programming Languages in NLP

Challenge:	Existing approaches to tool invocation are often unnecessarily long and require lengthy reasoning paths.
Approach:	They propose a framework for stepwise code generation that improves LLM tool invocation . they incorporate two distinct process rewards: the On-the-spot and the Latent Reward .
Outcome:	The proposed framework improves LLM tool invocation by leveraging the concise nature of code.

SentBS: Sentence-level Beam Search for Controllable Summarization (2022.emnlp-main)

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Chenhui Shen, Liying Cheng, Lidong Bing, Yang You, Luo Si

Text Generation

Challenge:	Structure-controlled summarization is a useful and interesting research direction . current structure-controlling methods have limited effectiveness in enforcing the desired structure.
Approach:	They propose a sentence-level beam search generation method to select suitable sentences for subsequent generations.
Outcome:	The proposed method significantly reduces structural discrepancies by 68% on a structure-controlled dataset.

Current Agents Fail to Leverage World Model as Tool for Foresight (2026.acl-long)

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Cheng Qian, Emre Can Acikgoz, Bingxuan Li, Xiusi Chen, Yuji Zhang, Bingxiang He, Qinyu Luo, Gokhan Tur, Dilek Hakkani-Tür, Yunzhu Li, Heng Ji

Reasoning

Challenge:	Generative world models could be used to enhance agents' cognition . agents are expected to operate in settings where tasks unfold over long horizons and involve intricate chains of interdependent decisions.
Approach:	They propose to use vision-language models as external simulators to enhance cognition . they find that agents rarely invoke simulation and misuse predicted rollouts .
Outcome:	The proposed model could be used to predict future states rather than short-horizon reasoning . the model could also be used for real-world planning and robotics .

A Generative Framework for Personalized Sticker Retrieval (2025.findings-emnlp)

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Changjiang Zhou, Ruqing Zhang, Jiafeng Guo, Yu-An Liu, Fan Zhang, Ganyuan Luo, Xueqi Cheng

Information Retrieval

Challenge:	Existing relevance-based generative retrieval methods lack personalization, leading to a mismatch between diverse user expectations and the retrieved results.
Approach:	They propose a representation learning model that learns discriminative user representations to encode user-specific sticker preferences.
Outcome:	The proposed framework outperforms state-of-the-art methods in generating relevant stickers for queries.

An Empirical Investigation of Implicit and Explicit Knowledge-Enhanced Methods for Ad Hoc Dataset Retrieval (2023.findings-emnlp)

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Weiqing Luo, Qiaosheng Chen, Zhiyang Zhang, Zixian Huang, Gong Cheng

IAM: A Comprehensive and Large-Scale Dataset for Integrated Argument Mining Tasks (2022.acl-long)

Challenge:	Existing methods for ad hoc dataset retrieval are lexical and cannot capture semantic similarity.
Approach:	They propose to implement and evaluate a set of implicit and explicit knowledge-enhancement retrieval methods on two test collections to find semantic matches for ad hoc dataset retrieval.
Outcome:	The proposed methods are compared with existing methods on two test collections and reveal the unique features of the task and suggest an interpolation of different kinds of methods as the current best practice.

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Liying Cheng, Lidong Bing, Ruidan He, Qian Yu, Yan Zhang, Luo Si

WordArt Designer: User-Driven Artistic Typography Synthesis using Large Language Models (2023.emnlp-industry)

Challenge:	Argument mining (AM) is a computational process that is used to analyze information in a debating system.
Approach:	They propose to use a large dataset to automate the manual process of debating . they propose to integrate claim extraction, stance classification and evidence extraction tasks .
Outcome:	The proposed tasks can extract claims, stances, evidence and more from a large dataset . the proposed tasks are highly efficient and can be applied to argument mining tasks .

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Jun-Yan He, Zhi-Qi Cheng, Chenyang Li, Jingdong Sun, Wangmeng Xiang, Xianhui Lin, Xiaoyang Kang, Zengke Jin, Yusen Hu, Bin Luo, Yifeng Geng, Xuansong Xie

GLTW: Joint Improved Graph Transformer and LLM via Three-Word Language for Knowledge Graph Completion (2025.findings-acl)

Challenge:	Existing typography solutions lack adaptability, creativity, and computational efficiency.
Approach:	They propose a user-driven framework for artistic typography synthesis based on the Large Language Model (LLM) the LLM Engine interprets user inputs and generates actionable prompts for the other modules, transforming abstract concepts into tangible designs.
Outcome:	The proposed framework incorporates four key modules: the LLM Engine, SemTypo, StyTyPo, and TexTyPO.

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Kangyang Luo, Yuzhuo Bai, Cheng Gao, Shuzheng Si, Zhu Liu, Yingli Shen, Zhitong Wang, Cunliang Kong, Wenhao Li, Yufei Huang, Ye Tian, Xuantang Xiong, Lei Han, Maosong Sun

When Rules Learn: A Self-Evolving Agent for Legal Case Retrieval (2026.findings-acl)

Challenge:	Existing knowledge graphs lack the ability to integrate structural information into LLMs and output predictions deterministically.
Approach:	They propose a method which encodes structural information of KGs and merges it with LLMs to enhance KGC performance.
Outcome:	The proposed method improves the performance of KG Completion datasets on KGs by integrating structural information with LLMs.

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Mingxu Tao, Jiawei Hu, Xian Zhou, Wenpeng Hu, Jiajun Cheng, Yunbo Cao, Zhunchen Luo, Guotong Geng

Paraphrasing

Challenge:	Existing dense retrieval methods have achieved notable progress, but their effectiveness in legal case retrieval remains limited.
Approach:	They propose a self-evolving framework for rule-driven query rewriting that enhances BM25 without any parameter training.
Outcome:	The proposed framework outperforms non-evolutionary baselines, including human-designed rules and greedy rule selection, especially when powered by a high-capacity core LLM.

ImCoref-CeS: An Improved Lightweight Pipeline for Coreference Resolution with LLM-based Checker-Splitter Refinement (2026.acl-long)

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Kangyang Luo, Yuzhuo Bai, Shuzheng Si, Cheng Gao, Zhitong Wang, Yingli Shen, Wenhao Li, Zhu Liu, Yufeng Han, Jiayi Wu, Cunliang Kong, Maosong Sun

TurnBack: A Geospatial Route Cognition Benchmark for Large Language Models through Reverse Route (2025.emnlp-main)

Challenge:	Existing supervised neural methods for coreference resolution are underexplored . current methods rely on small language models, but their potential is underexploited .
Approach:	They propose a framework that integrates an enhanced supervised model with LLM-based reasoning.
Outcome:	The proposed method surpasses existing state-of-the-art methods in coreference resolution.

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Hongyi Luo, Qing Cheng, Daniel Matos, Hari Krishna Gadi, Yanfeng Zhang, Lu Liu, Yongliang Wang, Niclas Zeller, Daniel Cremers, Liqiu Meng

OpenResearcher: Unleashing AI for Accelerated Scientific Research (2024.emnlp-demo)

Challenge:	Existing studies on large language models have limited evaluation of their geospatial cognition . a unified framework for evaluating geospcial cognition in LLMs remains absent .
Approach:	They propose a benchmark to evaluate the geospatial route cognition of Large Language Models . they propose 'pathbuilder' tool for converting natural language instructions into navigation routes .
Outcome:	The proposed framework and metrics evaluate 9 state-of-the-art LLMs on route reversal task.

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Yuxiang Zheng, Shichao Sun, Lin Qiu, Dongyu Ru, Cheng Jiayang, Xuefeng Li, Jifan Lin, Binjie Wang, Yun Luo, Renjie Pan, Yang Xu, Qingkai Min, Zizhao Zhang, Yiwen Wang, Wenjie Li, Pengfei Liu

Information Retrieval

Challenge:	Global scientific publications are growing annually by about 4%-5% (Pinedo et al., 2024).
Approach:	They introduce an AI-assisted platform that answers diverse questions from researchers using Retrieval-Augmented Generation (RAG) they develop various tools to understand queries, search from the scientific literature, filter retrieved information, provide accurate and comprehensive answers, and self-refine answers.
Outcome:	OpenResearcher is built on Retrieval-Augmented Generation (RAG) to integrate Large Language Models (LLMs) with up-to-date, domain-specific knowledge.

Language-aware Interlingua for Multilingual Neural Machine Translation (2020.acl-main)

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Changfeng Zhu, Heng Yu, Shanbo Cheng, Weihua Luo

Multilinguality

Challenge:	Existing multilingual neural machine translation models fail to capture diversity and specificity of different languages, resulting in inferior performance against individual models that are sufficiently trained.
Approach:	They propose to integrate a language-aware interlingua into an Encoder-Decoder architecture to learn a semantic representation from the semantic spaces of different languages while allowing for language-specific specialization of a particular language pair.
Outcome:	The proposed model achieves remarkable improvements over state-of-the-art multilingual NMT models and produces comparable performance with strong individual models.

Distance between Relevant Information Pieces Causes Bias in Long-Context LLMs (2025.findings-acl)

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Runchu Tian, Yanghao Li, Yuepeng Fu, Siyang Deng, Qinyu Luo, Cheng Qian, Shuo Wang, Xin Cong, Zhong Zhang, Yesai Wu, Yankai Lin, Huadong Wang, Xiaojiang Liu

Challenge:	Positional biases in large language models hinder their ability to process long inputs.
Approach:	They propose a benchmark to assess positional bias in large language models involving multiple pieces of relevant information.
Outcome:	The proposed benchmark assesses the performance of long-context language models by examining their models with different input lengths and tasks.

R3-RAG: Learning Step-by-Step Reasoning and Retrieval for LLMs via Reinforcement Learning (2025.findings-emnlp)

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Yuan Li, Qi Luo, Xiaonan Li, Bufan Li, Qinyuan Cheng, Bo Wang, Yining Zheng, Yuxin Wang, Zhangyue Yin, Xipeng Qiu

Unlocking Continual Learning Abilities in Language Models (2024.findings-emnlp)

Challenge:	RAG systems that integrate external knowledge with Large Language Models often become bottlenecks due to their limited parameters compared to LLMs and their inability to perform step-by-step reasoning.
Approach:	They propose a model that integrates external knowledge with Large Language Models to enhance factual correctness and mitigate hallucination.
Outcome:	The proposed model outperforms baselines and can transfer well to different retrievers.

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Wenyu Du, Shuang Cheng, Tongxu Luo, Zihan Qiu, Zeyu Huang, Ka Chun Cheung, Reynold Cheng, Jie Fu

Leave No Document Behind: Benchmarking Long-Context LLMs with Extended Multi-Doc QA (2024.emnlp-main)

Challenge:	Existing approaches to learning models (LMs) incorporate old task data or task-wise inductive bias into LMs, but old data and accurate task information are often unavailable or costly to collect.
Approach:	They propose a rehearsal-free method that updates model parameters with large magnitudes . they found that the L1-normalized magnitude distribution is different when different task data is used .
Outcome:	The proposed method improves accuracy and performance on four CL benchmarks.

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Minzheng Wang, Longze Chen, Fu Cheng, Shengyi Liao, Xinghua Zhang, Bingli Wu, Haiyang Yu, Nan Xu, Lei Zhang, Run Luo, Yunshui Li, Min Yang, Fei Huang, Yongbin Li

Aligning Large Language Models to Follow Instructions and Hallucinate Less via Effective Data Filtering (2025.acl-long)

Challenge:	Existing benchmarks for evaluating long-context language models employ irrelevant noise texts to artificially extend the length of test cases, diverging from the real-world scenarios of long-constituency applications.
Approach:	They propose a long-context benchmark, Loong, aligning with realistic scenarios through extended multi-document question answering (QA) .
Outcome:	The proposed model can scale up the context window of large language models to perform in-depth analysis of multiple long documents.

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Shuzheng Si, Haozhe Zhao, Gang Chen, Cheng Gao, Yuzhuo Bai, Zhitong Wang, Kaikai An, Kangyang Luo, Chen Qian, Fanchao Qi, Baobao Chang, Maosong Sun

Won’t Get Fooled Again: Answering Questions with False Premises (2023.acl-long)

Challenge:	Existing studies show that training LLMs on data containing unfamiliar knowledge during instruction tuning can encourage hallucinations.
Approach:	They propose a framework that measures how familiar the LLM is with instruction data and introduce an expert-aligned reward model to ensure the quality of selected samples.
Outcome:	The proposed framework reduces hallucinations while maintaining a competitive ability to follow instructions.

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Shengding Hu, Yifan Luo, Huadong Wang, Xingyi Cheng, Zhiyuan Liu, Maosong Sun

Question Answering

Challenge:	Pre-trained language models (PLMs) are often easily deceived by tricky questions such as “How many eyes does the sun have?” .
Approach:	They annotate a FalseQA dataset containing 2365 human-written FPQs and find that PLMs are capable of discriminating FPqs by fine-tuning on moderate numbers.
Outcome:	The proposed model can discriminate on FPQs by fine-tuning on moderate numbers of examples and generate reasonable explanations for false premise questions.

API Is Enough: Conformal Prediction for Large Language Models Without Logit-Access (2024.findings-emnlp)

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Jiayuan Su, Jing Luo, Hongwei Wang, Lu Cheng

OpenCoder: The Open Cookbook for Top-Tier Code Large Language Models (2025.acl-long)

Challenge:	Existing methods for quantifying uncertainty in large language models with black-box API access are limited due to the complex data distributions and inner model mechanism.
Approach:	They propose a conformal prediction method that minimizes the size of prediction sets and ensures a statistical guarantee of the user-defined coverage.
Outcome:	The proposed method outperforms existing methods on close-ended and open-ended questions.

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Siming Huang, Tianhao Cheng, Jason Klein Liu, Weidi Xu, Jiaran Hao, Liuyihan Song, Yang Xu, Jian Yang, Jiaheng Liu, Chenchen Zhang, Linzheng Chai, Ruifeng Yuan, Xianzhen Luo, Qiufeng Wang, YuanTao Fan, Qingfu Zhu, Zhaoxiang Zhang, Yang Gao, Jie Fu, Qian Liu, Houyi Li, Ge Zhang, Yuan Qi, Xu Yinghui, Wei Chu, Zili Wang

Large Language Models Can Be Contextual Privacy Protection Learners (2024.emnlp-main)

Challenge:	Code LLMs lack reproducible data pipelines and training protocols for reproducible advancements in code intelligence.
Approach:	They propose a top-tier code LLM that releases model weights and inference code . reproducible data pipelines, rigorous experimental ablation results and training protocols are included .
Outcome:	The proposed model achieves comparable performance to leading models and serves as an "open cookbook" reproducible training data, rigorous experimental ablation results, and detailed training protocols are also included in the model.

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Yijia Xiao, Yiqiao Jin, Yushi Bai, Yue Wu, Xianjun Yang, Xiao Luo, Wenchao Yu, Xujiang Zhao, Yanchi Liu, Quanquan Gu, Haifeng Chen, Wei Wang, Wei Cheng

NewsBench: A Systematic Evaluation Framework for Assessing Editorial Capabilities of Large Language Models in Chinese Journalism (2024.acl-long)

Challenge:	Large Language Models (LLMs) have demonstrated remarkable linguistic comprehension and generation capability, but when applied to specialized industries, they face challenges such as hallucination, insufficient domain knowledge, and failing to incorporate the latest domain knowledge.
Approach:	They propose a paradigm for fine-tuning LLMs that effectively injects domain-specific knowledge while safeguarding inference-time data privacy.
Outcome:	The proposed model protects private data while enhancing the model's knowledge.

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Miao Li, Ming-Bin Chen, Bo Tang, ShengbinHou ShengbinHou, Pengyu Wang, Haiying Deng, Zhiyu Li, Feiyu Xiong, Keming Mao, Cheng Peng, Yi Luo

Prune as You Generate: Online Rollout Pruning for Faster and Better RLVR (2026.acl-long)

Challenge:	a novel evaluation framework assesses the capabilities of Large Language Models (LLMs) for editorial capabilities in Chinese journalism.
Approach:	They propose to use a benchmark dataset to assess the capabilities of Large Language Models (LLMs) for editorial capabilities in Chinese journalism.
Outcome:	The proposed evaluation framework is based on a dataset of 1,267 test samples in 24 news domains.

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Haobo Xu, Sirui Chen, Ruizhong Qiu, Yuchen Yan, Chen Luo, Monica Xiao Cheng, Jingrui He, Hanghang Tong

Challenge:	Reinforcement Learning with Verifiable Rewards (RLVR) has improved reasoning capabilities of Large Language Models (LLMs).
Approach:	They propose an online pruning method that prunes rollouts while steering correct ones to enhance learning signals.
Outcome:	The proposed method improves average accuracy by +2.30 to +2.99 across GRPO and DAPO on Qwen-3 and LLaMA-3.2 models.

LLMBox: A Comprehensive Library for Large Language Models (2024.acl-demos)

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Tianyi Tang, Hu Yiwen, Bingqian Li, Wenyang Luo, ZiJing Qin, Haoxiang Sun, Jiapeng Wang, Shiyi Xu, Xiaoxue Cheng, Geyang Guo, Han Peng, Bowen Zheng, Yiru Tang, Yingqian Min, Yushuo Chen, Jie Chen, Ranchi Zhao, Luran Ding, Yuhao Wang, Zican Dong, Xia Chunxuan, Junyi Li, Kun Zhou, Xin Zhao, Ji-Rong Wen

Chinese Court Simulation with LLM-Based Agents System (2026.findings-acl)

Challenge:	a library to facilitate the development, use, and evaluation of large language models (LLMs) is presented.
Approach:	They propose a unified library to facilitate the development, use and evaluation of large language models (LLMs).
Outcome:	The proposed library is based on extensive experiments in a variety of evaluation settings.

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Kaiyuan Zhang, Jiaqi Li, Yueyue Wu, Haitao Li, Cheng Luo, Shaokun Zou, Yujia Zhou, Weihang Su, Yiqun Liu, Qingyao Ai

Enrich, Aggregate, and Generate: Three-stage Biomedical Data-to-Text Generation Using Large Language Models in Low-resource Scenarios (2026.findings-acl)

Challenge:	Existing studies have neglected the systematic design and procedure evaluation of court simulations, which are critical to the credibility and usage of court simulators in practice.
Approach:	They propose a court simulation paradigm based on the real-world procedure structure of Chinese courts and a framework that focuses on both legal judgment prediction and court procedure analysis.
Outcome:	The proposed model outperforms judges and lawyers from the real trials in many aspects.

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Yupian Lin, Guangya Yu, Yuang Bian, Cheng Yuan, Hui Luo, Tong Ruan

Text Generation

Challenge:	Biomedical data-to-text generation is a branch of Natural Language Generation, aiming at generating textual natural language descriptions that can fluently and precisely describe the structured data.
Approach:	They propose an LLM framework that can be used to generate textual natural language descriptions using in-context learning.
Outcome:	The proposed framework provides good interpretability and superior performance on the BioLeaflets dataset.

Intention Knowledge Graph Construction for User Intention Relation Modeling (2026.eacl-long)

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Jiaxin Bai, Zhaobo Wang, Junfei Cheng, Dan Yu, Zerui Huang, Weiqi Wang, Xin Liu, Chen Luo, Yanming Zhu, Bo Li, Yangqiu Song

Inverse is Better! Fast and Accurate Prompt for Few-shot Slot Tagging (2022.findings-acl)

Challenge:	Existing knowledge graphs focus on connecting intentions but lacks the ability to model the relationships between different intentions.
Approach:	They propose a framework to automatically generate an intention knowledge graph, capturing connections between user intentions.
Outcome:	The proposed model outperforms state-of-the-art methods and shows its utility.

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Yutai Hou, Cheng Chen, Xianzhen Luo, Bohan Li, Wanxiang Che

Retrieval-Augmented Generation for Large Language Model based Few-shot Chinese Spell Checking (2025.coling-main)

Challenge:	Recent results show that prompting methods are inefficient for slot tagging tasks . inverse prompting only requires a one-turn prediction for each slot type .
Approach:	They propose an inverse prompting paradigm that reversely predicts slot values given slot types . the method is faster and significantly improves the effect on 10-shot setting .
Outcome:	The proposed method improves over 6.1 F1-scores on 10-shot setting and achieves new state-of-the-art performance.

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Ming Dong, Zhiwei Cheng, Changyin Luo, Tingting He

Low-Resource NLP

Challenge:	Existing LLM-based Chinese spelling check methods rely on fixed prompt samples . existing methods are limited by technical bottlenecks, complex recognition environments, and individual differences .
Approach:	They propose a framework called RagID to provide well-chosen prompt samples . they propose to use semantic-based similarity search and iterative discriminator mechanism .
Outcome:	The proposed framework can provide well-chosen prompt samples and reduce overcorrection issues in Chinese spelling check tasks.

OneRec-Think: In-Text Reasoning for Generative Recommendation (2026.acl-long)

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Zhanyu Liu, Shiyao Wang, Xingmei Wang, Rongzhou Zhang, Jiaxin Deng, Honghui Bao, Jinghao Zhang, Wuchao Li, PengFei Zheng, Xiangyu Wu, Yifei Hu, Qigen Hu, Xinchen Luo, Lejian Ren, Zhang Zixing, Qianqian Wang, Kuo Cai, Yunfan Wu, Hongtao Cheng, Zexuan Cheng, Lu Ren, Huanjie Wang, Yi Su, Ruiming Tang, Kun Gai, Guorui Zhou

Reasoning

Challenge:	Existing generative models lack the capacity for explicit and controllable reasoning, a key advantage of LLMs.
Approach:	They propose a framework that integrates dialogue, reasoning, and personalized recommendation.
Outcome:	Experiments across public benchmarks show state-of-the-art performance.

HoH: A Dynamic Benchmark for Evaluating the Impact of Outdated Information on Retrieval-Augmented Generation (2025.acl-long)

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Jie Ouyang, Tingyue Pan, Mingyue Cheng, Ruiran Yan, Yucong Luo, Jiaying Lin, Qi Liu

Information Retrieval

Challenge:	Current approaches to addressing knowledge outdating in LLMs struggle with retrieval and generation aspects when handling outdated information.
Approach:	They propose a benchmark to evaluate the impact of outdated information on RAG . they use token-level diff algorithms and LLM pipelines to create a large-scale QA dataset .
Outcome:	The proposed benchmark analyzes the impact of outdated information on RAG performance.

LogToP: Logic Tree-of-Program with Table Instruction-tuned LLMs for Controlled Logical Table-to-Text Generation (2026.findings-eacl)

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Yupian Lin, Guangya Yu, Cheng Yuan, Huan Du, Hui Luo, Yuang Bian, Jingping Liu, Zhidong He, Wen Du, Tong Ruan

ENT-DESC: Entity Description Generation by Exploring Knowledge Graph (2020.emnlp-main)

Challenge:	Existing LLMs are difficult to achieve satisfactory results in table-related tasks.
Approach:	They propose to develop a specialized logical table-to-text generation model that can be used for table-related tasks.
Outcome:	The proposed model achieves state-of-the-art on a Logic2Text dataset.

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Liying Cheng, Dekun Wu, Lidong Bing, Yan Zhang, Zhanming Jie, Wei Lu, Luo Si

Structured Data in NLP

Challenge:	Existing models for knowledge-to-text generation use RDF triples or key-value pairs to generate a natural language description.
Approach:	They propose a large-scale dataset to facilitate the study of KG-to-text . they propose MGCN model architecture that incorporates aggregation methods to extract the rich graph information.
Outcome:	The proposed model can represent the original graph information more comprehensively and integrates multiple aggregation methods to extract the rich graph information.

APE: Argument Pair Extraction from Peer Review and Rebuttal via Multi-task Learning (2020.emnlp-main)

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Liying Cheng, Lidong Bing, Qian Yu, Wei Lu, Luo Si