Papers by Zheng Luo

Model Composition for Multimodal Large Language Models (2024.acl-long)

Challenge:	Large Audio Language Models (LALMs) exhibit a degradation in knowledge and reasoning capabilities . empirical results show that CORD significantly bridges the audio–text performance gap .
Approach:	They propose a framework that performs online cross-modal self-distillation to bridge the acoustic-semantic gap between LALMs and text-based models.
Outcome:	The proposed framework bridges the acoustic-semantic gap between LALMs and text-based models . it employs on-policy reverse KL divergence with importance-aware weighting .

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Chi Chen, Yiyang Du, Zheng Fang, Ziyue Wang, Fuwen Luo, Peng Li, Ming Yan, Ji Zhang, Fei Huang, Maosong Sun, Yang Liu

IMOL: Incomplete-Modality-Tolerant Learning for Multi-Domain Fake News Video Detection (2025.acl-long)

Challenge:	Existing methods for creating versatile MLLMs rely on joint training with paired instruction data, which is resource-intensive and challenging to extend to new modalities.
Approach:	They propose a new paradigm for multimodal large language models by reusing modality encoders and merging LLM parameters.
Outcome:	The proposed model retains the modal understanding capabilities of each original model.

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Zhi Zeng, Jiaying Wu, Minnan Luo, Herun Wan, Xiangzheng Kong, Zihan Ma, Guang Dai, Qinghua Zheng

LightThinker: Thinking Step-by-Step Compression (2025.emnlp-main)

Challenge:	Existing methods for fake news video detection focus on a specific domain and assume multiple modalities.
Approach:	They propose an incomplete-modality-tolerant learning framework for fake news video detection . they use cross-modal consistency to reconstruct missing modalities and transferable knowledge through cross-sample reasoning .
Outcome:	The proposed framework improves performance and robustness of multi-domain fake news video detection while generalizing to unseen domains under incomplete modality conditions.

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Jintian Zhang, Yuqi Zhu, Mengshu Sun, Yujie Luo, Shuofei Qiao, Lun Du, Da Zheng, Huajun Chen, Ningyu Zhang

What Makes AI Research Replicable? Executable Knowledge Graphs as Scientific Knowledge Representations (2026.acl-short)

Challenge:	Recent advances in Large Language Models have demonstrated their remarkable capabilities in complex reasoning tasks, but their efficiency is hindered by the substantial memory and computational costs associated with generating lengthy tokens.
Approach:	They propose a method that dynamically compresses verbose thought steps into compact representations and discards original reasoning chains.
Outcome:	The proposed method reduces peak memory usage and inference time while maintaining competitive accuracy.

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Yujie Luo, Zhuoyun Yu, Xuehai Wang, Yuqi Zhu, Ningyu Zhang, Lanning Wei, Lun Du, Da Zheng, Huajun Chen

An LLM-Enhanced Adversarial Editing System for Lexical Simplification (2024.lrec-main)

Challenge:	Existing approaches to replicate AI research are limited by insufficient background knowledge and the limitations of retrieval-augmented generation methods.
Approach:	They propose a pluggable, paper-centric knowledge base that integrates code snippets and technical insights extracted from scientific literature into a verifiable, executable representation.
Outcome:	The proposed knowledge base shows significant performance gains on paperBench when integrated into three agent frameworks with two different LLMs.

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Keren Tan, Kangyang Luo, Yunshi Lan, Zheng Yuan, Jinlong Shu

IterAlign: Iterative Constitutional Alignment of Large Language Models (2024.naacl-long)

Challenge:	Existing methods to simplify text rely heavily on annotated data, making it challenging to apply in low-resource scenarios.
Approach:	They propose a Lexical Simplification method without parallel corpora that uses an Adversarial Editing System and an LLM-enhanced loss to distill knowledge into a small-size LS system.
Outcome:	The proposed method uses an LLM-enhanced loss to distill knowledge from Large Language Models (LLMs) into a small-size LS system.

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Xiusi Chen, Hongzhi Wen, Sreyashi Nag, Chen Luo, Qingyu Yin, Ruirui Li, Zheng Li, Wei Wang

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

Challenge:	Empirical results show that iterAlign improves truthfulness, helpfulness, harmlessness and honesty, improving the LLM alignment by up to 13.5% in harmlessness.
Approach:	They propose a data-driven constitution discovery and self-alignment framework called IterAlign to overcome these drawbacks by leveraging red teaming to uncover weaknesses of an LLM.
Outcome:	Empirical results show that iterAlign improves truthfulness, helpfulness, harmlessness and honesty by up to 13.5%.

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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

Can We Predict Before Executing Machine Learning Agents? (2026.acl-long)

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.

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Jingsheng Zheng, Jintian Zhang, Yujie Luo, Yuren Mao, Yunjun Gao, Lun Du, Huajun Chen, Ningyu Zhang

Challenge:	Existing approaches to scientific discovery rely on expensive physical execution . a Generate-Execute-Feedback paradigm is costly and slow .
Approach:	They propose to internalize execution priors to substitute costly runtime checks with instantaneous predictive reasoning, drawing inspiration from World Models.
Outcome:	The proposed framework achieves 61.5% accuracy and robust confidence calibration when primed with a Verified Data Analysis Report.

HDiff: Confidence-Guided Denoising Diffusion for Robust Hyper-relational Link Prediction (2025.findings-emnlp)

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Xiangfeng Luo, Ruoxin Zheng, Jianqiang Huang, Hang Yu

LongTutor: Benchmarking Large Language Models for Long-term Personalized Tutoring (2026.acl-long)

Challenge:	Hyper-relational Knowledge Graph Completion (HKGC) is more sensitive to inherent noise, particularly struggling with two prevalent HKG-specific noise types: Intra-fact Inconsistency and Cross-fact Association Noise.
Approach:	They propose a conditional denoising diffusion framework that learns to reverse structured noise corruption.
Outcome:	The proposed framework outperforms state-of-the-art HKGC methods in a variety of noisy conditions.

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Ning Li, Zheng Zhang, Zhenya Huang, Rui Li, Yi Zhan, Yinbo Luo, Qi Liu, Enhong Chen

Can Many-Shot In-Context Learning Help LLMs as Evaluators? A Preliminary Empirical Study (2025.coling-main)

Challenge:	Existing evaluations focus on isolated, short-term interactions, overlooking the inherently long-term nature of learning.
Approach:	They propose a benchmark for long-term personalized tutoring based on an annotated learning log . they propose an automated generator–verifier pipeline to enable benchmark expansion .
Outcome:	The proposed benchmarks evaluate LLMs across three progressive tasks: evidence acquisition, knowledge state diagnosis, and adaptive teaching action.

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Mingyang Song, Mao Zheng, Xuan Luo

Detecting Spoilers in Movie Reviews with External Movie Knowledge and User Networks (2023.emnlp-main)

Challenge:	Existing evaluation approaches to evaluate Large Language Models are affected by potential biases within LLMs.
Approach:	They propose two many-shot In-Context Learning (ICL) prompt templates to help LLM evaluators mitigate potential biases.
Outcome:	The proposed templates reduce biases by using in-context examples with model-generated rationales as references.

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Heng Wang, Wenqian Zhang, Yuyang Bai, Zhaoxuan Tan, Shangbin Feng, Qinghua Zheng, Minnan Luo

Knowledge Representation

Challenge:	Existing models focus on the textual content of the review, while spoiler detection requires putting the review into the context of facts and knowledge regarding movies.
Approach:	They propose a network-based spoiler detection model that takes into account external knowledge about movies and user activities on movie review platforms.
Outcome:	The proposed model takes into account external knowledge about movies and user activities on movie review platforms while incorporating user networks.

FastCuRL: Curriculum Reinforcement Learning with Stage-wise Context Scaling for Efficient Training R1-like Reasoning Models (2025.findings-emnlp)

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Mingyang Song, Mao Zheng, Zheng Li, Wenjie Yang, Xuan Luo

A Context-based Framework for Modeling the Role and Function of On-line Resource Citations in Scientific Literature (D19-1)

Challenge:	Improving training efficiency remains a challenge in large-scale Reinforcement Learning (RL).
Approach:	They propose a curriculum RL framework with stage-wise context scaling to improve RL training efficiency.
Outcome:	The proposed framework outperforms state-of-the-art reasoning models on five benchmarks and achieves 49.6% accuracy on AIME 2024.

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He Zhao, Zhunchen Luo, Chong Feng, Anqing Zheng, Xiaopeng Liu

Text Classification

Challenge:	Existing academic search engines cannot detect relevant papers where a resource is mentioned.
Approach:	They propose a framework to model the role and function of on-line resource citations . they construct a dataset SciRes, which includes 3,088 manually annotated resource contexts based on a multi-task framework .
Outcome:	The proposed model achieves the best results on both the classification task and recommendation task.

Beyond Black-Box Interventions: Latent Probing for Faithful Retrieval-Augmented Generation (2026.findings-acl)

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Linfeng Gao, Qinggang Zhang, Baolong Bi, Bo Zeng, Zheng Yuan, Zerui Chen, Zhimin Wei, Shenghua Liu, Linlong Xu, Longyue Wang, Weihua Luo, Jinsong Su

RoBSA: RoPE-based Blockwise Sparse Multi-head Latent Attention (2026.acl-long)

Challenge:	Existing approaches to improve contextual faithfulness treat the LLM as a black box, generating responses that are inconsistent with the provided context.
Approach:	They propose a framework for faithful RAG that operates in three stages: (i) fine-grained knowledge pruning to filter irrelevant context, (ii) latent conflict probing to identify hard conflicts in the model’s latent space, and (iv) conflict-aware attention to modulate attention heads toward faithful context integration.
Outcome:	Experiments show that ProbeRAG significantly improves both accuracy and contextual faithfulness.

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Xinyu Shi, Kairong Luo, Zhen Zheng, Wenguang Chen

Leveraging Human and Machine Preferences for Zero-shot Detection of AI-Generated Text (2026.findings-acl)

Challenge:	Large Language Models (LLMs) have advanced in recent years, scaling up in both parameter count and context length.
Approach:	They propose a method to compute attention over a subset of context tokens and to implement token selection in a blockwise manner.
Outcome:	The proposed method reduces end-to-end inference latency by up to 2.55x with minimal accuracy loss compared to full attention in long-context scenarios for very large models.

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Lei Jiang, Desheng Wu, Xiaolong Zheng, Cuicui Luo

Beyond Decoder-only: Large Language Models Can be Good Encoders for Machine Translation (2025.findings-acl)

Challenge:	Recent advances in large language models have enabled generated texts to closely mimic human writing, posing significant challenges to the detection of AI-generated content.
Approach:	They propose a human-machine prediction discrepancy adapter for AI-generated text detection . they use a joint fine-tuning strategy and a discrepany-aware reweighting mechanism .
Outcome:	The proposed framework improves the detection performance of five representative models under various evaluation scenarios.

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Yingfeng Luo, Tong Zheng, Yongyu Mu, Bei Li, Qinghong Zhang, Yongqi Gao, Ziqiang Xu, Peinan Feng, Xiaoqian Liu, Tong Xiao, JingBo Zhu

STAR: SQL Guided Pre-Training for Context-dependent Text-to-SQL Parsing (2022.findings-emnlp)

Challenge:	Recent advances in machine translation have focused on a single pre-trained decoder . encoder-decoder architectures have received relatively little attention in NMT .
Approach:	They propose a method that leverages LLMs as MT encoders and pairs them with lightweight decoders to develop universal translation models.
Outcome:	The proposed method matches or surpasses baselines in terms of translation quality but achieves 75% reduction in memory footprint of the KV cache.

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Zefeng Cai, Xiangyu Li, Binyuan Hui, Min Yang, Bowen Li, Binhua Li, Zheng Cao, Weijie Li, Fei Huang, Luo Si, Yongbin Li

Programming Languages in NLP

Challenge:	Extensive experiments show that STAR outperforms previous pre-training methods and ranks first on the leaderboard . text-to-SQL parsing aims to translate natural language (NL) questions into executable SQL queries .
Approach:	They propose a SQL guided pre-training framework STAR for context-dependent text-to-SQL parsing . they propose two objectives that explore context-dependence of NL utterances and SQL queries .
Outcome:	The proposed framework outperforms existing methods on two downstream benchmarks and ranks first on the leaderboard.

A Survey of RAG-Reasoning Systems in Large Language Models (2025.findings-emnlp)

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Yangning Li, Weizhi Zhang, Yuyao Yang, Wei-Chieh Huang, Yaozu Wu, Junyu Luo, Yuanchen Bei, Henry Peng Zou, Xiao Luo, Yusheng Zhao, Chunkit Chan, Yankai Chen, Zhongfen Deng, Yinghui Li, Hai-Tao Zheng, Dongyuan Li, Renhe Jiang, Ming Zhang, Yangqiu Song, Philip S. Yu

Adaptive Nearest Neighbor Machine Translation (2021.acl-short)

Challenge:	a survey of RAG-based reasoning-based approaches shows that it is not effective for multi-step inferences.
Approach:	They map how advanced reasoning optimizes each stage of RAG . they show how retrieved knowledge supply missing premises and expand context for complex inference .
Outcome:	The proposed frameworks achieve state-of-the-art across knowledge-intensive benchmarks.

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Xin Zheng, Zhirui Zhang, Junliang Guo, Shujian Huang, Boxing Chen, Weihua Luo, Jiajun Chen

Machine Translation

Challenge:	kNN-MT uses pre-trained NMT model with token-level k-nearest-neighbor retrieval to improve translation accuracy.
Approach:	They propose a method that combines a pre-trained NMT model with token-level k-nearest-neighbor retrieval to improve translation accuracy.
Outcome:	The proposed method outperforms the existing model on four benchmark datasets and is open-source.

Incremental Sequence Labeling: A Tale of Two Shifts (2024.findings-acl)

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Shengjie Qiu, Junhao Zheng, Zhen Liu, Yicheng Luo, Qianli Ma

M3-Embedding: Multi-Linguality, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation (2024.findings-acl)

Challenge:	Existing approaches to incremental sequence labeling have focused on the E2O and O2E issues, but neglect the O2e issue.
Approach:	They propose a framework for incremental sequence labeling without semantic shifts that mitigate catastrophic forgetting in models by using knowledge distillation to maintain the model’s discriminative ability for old entities.
Outcome:	The proposed framework mitigates catastrophic forgetting in models while maintaining discriminative ability for old entities while minimizing the model’s bias towards new entities.

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Jianlyu Chen, Shitao Xiao, Peitian Zhang, Kun Luo, Defu Lian, Zheng Liu

AgentThink: A Unified Framework for Tool-Augmented Chain-of-Thought Reasoning in Vision-Language Models for Autonomous Driving (2025.findings-emnlp)

Challenge:	In this paper, we introduce a new embedding model for semantic retrieval of more than 100 working languages.
Approach:	They propose a new embedding model that supports multi-lingual, cross-lingual and long-document retrieval . they propose integrating relevance scores from different retrieval functionalities into the teacher signal .
Outcome:	The proposed model exhibits superior performance on multilingual, cross-lingual, and long-document retrieval benchmarks.

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Kangan Qian, Sicong Jiang, Yang Zhong, Ziang Luo, Zilin Huang, Tianze Zhu, Kun Jiang, Mengmeng Yang, Zheng Fu, Jinyu Miao, Yining Shi, He Zhe Lim, Li Liu, Tianbao Zhou, Hongyi Wang, Huang Yu, Yifei Hu, Guang Li, Guang Chen, Hao Ye, Lijun Sun, Diange Yang

Quantifying the Impact of Structured Output Format on Large Language Models through Causal Inference (2026.findings-eacl)

Challenge:	Vision-Language Models struggle with hallucinations, inefficient reasoning, and limited real-world validation hinders accurate perception and robust step-by-step reasoning.
Approach:	AgentThink integrates Chain-of-Thought reasoning with dynamic, agent-style tool invocation for autonomous driving tasks.
Outcome:	Experiments on the DriveLMM-o1 benchmark show AgentThink significantly boosts overall reasoning scores by 53.91% and enhances answer accuracy by 33.54% .

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Han Yuan, Yue Zhao, Li Zhang, Wuqiong Luo, Zheng Ma

Tandem: Riding Together with Large and Small Language Models for Efficient Reasoning (2026.findings-acl)

Challenge:	Prior studies have examined the impact of structured output on LLMs’ generation quality, often presenting one-way findings.
Approach:	They propose to derive five potential causal structures characterizing the influence of structured output on LLMs’ generation using one assumed and two guaranteed constraints.
Outcome:	The proposed pipeline can be extended to other modules and is not limited to structured output but can be used in industrial applications.

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Zichuan Fu, Xian Wu, Guojing Li, Yejing Wang, Yijun Chen, Zhao Zihao, Luo Yixuan, Hanyu Yan, Yefeng Zheng, Xiangyu Zhao

Wukong-Reader: Multi-modal Pre-training for Fine-grained Visual Document Understanding (2023.acl-long)

Challenge:	Recent advances in large language models (LLMs) have catalyzed the rise of reasoningintensive inference paradigms, where models perform explicit step-by-step reasoning before generating final answers.
Approach:	They propose a large-small LLM collaboration framework that synergizes large and small language models to achieve high-quality reasoning with significantly reduced computational cost.
Outcome:	The proposed framework outperforms the mentor LLM while preserving the benefits of the thinking paradigm of LLMs.

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Haoli Bai, Zhiguang Liu, Xiaojun Meng, Li Wentao, Shuang Liu, Yifeng Luo, Nian Xie, Rongfu Zheng, Liangwei Wang, Lu Hou, Jiansheng Wei, Xin Jiang, Qun Liu

Marrying Up Regular Expressions with Neural Networks: A Case Study for Spoken Language Understanding (P18-1)

Challenge:	Existing solutions for visual document understanding lack granularity of document textlines.
Approach:	They propose a supervised pre-training program to leverage structural knowledge nested in document textlines to achieve fine-grained alignment between visual regions and texts.
Outcome:	The proposed system performs better on various VDU tasks in English and Chinese.

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Bingfeng Luo, Yansong Feng, Zheng Wang, Songfang Huang, Rui Yan, Dongyan Zhao

Challenge:	Experimental results show that the combination of regular expressions and NNs improves learning effectiveness when a small number of training examples are available.
Approach:	They propose to combine a neural network (NN) with regular expressions (RE) to improve supervised learning for NLP by exploiting the rich expressiveness of REs at different levels within a NN.
Outcome:	The proposed approach significantly improves learning effectiveness when a small number of training examples are available.

Unveiling Fake News with Adversarial Arguments Generated by Multimodal Large Language Models (2025.coling-main)

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Xiaofan Zheng, Minnan Luo, Xinghao Wang

Eidos, INDRA, & Delphi: From Free Text to Executable Causal Models (N19-4)

Challenge:	Existing methods for detecting fake news rely on neural networks to learn latent feature representations with limited real-world understanding.
Approach:	They propose a method that leverages Multimodal Large Language Models for fake news detection that introduces adversarial reasoning through debates from opposing perspectives.
Outcome:	The proposed method significantly outperforms state-of-the-art methods on four fake news detection datasets.

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Rebecca Sharp, Adarsh Pyarelal, Benjamin Gyori, Keith Alcock, Egoitz Laparra, Marco A. Valenzuela-Escárcega, Ajay Nagesh, Vikas Yadav, John Bachman, Zheng Tang, Heather Lent, Fan Luo, Mithun Paul, Steven Bethard, Kobus Barnard, Clayton Morrison, Mihai Surdeanu

Challenge:	a paper proposes a method for building probabilistic models of complex phenomena such as food insecurity . currently, these models are hand-built for each new situation and require months to construct .
Approach:	They propose an approach that builds executable probabilistic models from raw, free text.
Outcome:	The proposed approach builds executable probabilistic models from raw, free text.

AIGuard: A Benchmark and Lightweight Detection for E-commerce AIGC Risks (2025.findings-acl)

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Wenhua Zhang, Weicheng Li, Xuanrong Rao, Lixin Zou, Xiangyang Luo, Chubin Zhuang, Yongjie Hong, Zhen Qin, Hengyu Chang, Chenliang Li, Bo Zheng

Landmark Embedding: A Chunking-Free Embedding Method For Retrieval Augmented Long-Context Large Language Models (2024.acl-long)

Challenge:	Existing detection methods lack real-world scenarios and corresponding risk datasets . current MLLMs lack knowledge and have limited capability to detect the risk of AIGC content.
Approach:	They propose a benchmark for AIGC risk detection in real-world e-commerce . it includes 253,420 image-text pairs across four critical categories .
Outcome:	The proposed method achieves 9.68% higher recall than leading multimodal models while using only 25% of training resources.

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Kun Luo, Zheng Liu, Shitao Xiao, Tong Zhou, Yubo Chen, Jun Zhao, Kang Liu

LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models (2024.acl-demos)

Challenge:	Existing methods for retrieval augmentation work with chunked contexts, which leads to poor quality of semantic representation and incomplete retrieval of useful information.
Approach:	They propose a method for retrieval augmentation of long-context language modeling using landmark embedding.
Outcome:	The proposed method outperforms existing retrieval methods with a notable advantage.

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Yaowei Zheng, Richong Zhang, Junhao Zhang, YeYanhan YeYanhan, Zheyan Luo

Cross-Lingual Contrastive Learning for Fine-Grained Entity Typing for Low-Resource Languages (2022.acl-long)

Challenge:	Efficient fine-tuning of large language models requires non-trivial efforts to implement these methods on different models.
Approach:	They propose a framework that democratizes the fine-tuning of large language models by integrating a suite of efficient training methods into one framework.
Outcome:	The proposed framework is able to scale to 100+ LLMs without coding and receives over 25,000 stars and 3,000 forks.

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Xu Han, Yuqi Luo, Weize Chen, Zhiyuan Liu, Maosong Sun, Zhou Botong, Hao Fei, Suncong Zheng

AutoSchemaKG: Autonomous Knowledge Graph Construction through Dynamic Schema Induction from Web-Scale Corpora (2026.acl-long)

Challenge:	Experimental results show that by applying our framework, we can easily learn effective FGET models for low-resource languages.
Approach:	They propose a cross-lingual contrastive learning framework to learn FGET models for low-resource languages.
Outcome:	The proposed framework can learn effective FGET models for low-resource languages even without human-labeled data.

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Jiaxin Bai, Wei Fan, Qi Hu, Qing Zong, Chunyang Li, Hong Ting Tsang, Hongyu Luo, Yauwai Yim, Haoyu Huang, Xiao Zhou, Feng Qin, Tianshi Zheng, Xi Peng, Xin Yao, Huiwen Yang, Leijie Wu, JI Yi, Gong Zhang, Renhai Chen, Yangqiu Song

Everyone is unique: Towards Behaviorally Heterogeneous Negotiation Dialogue Systems for Debt Collection (2026.acl-long)

Challenge:	Existing knowledge graph construction frameworks require predefined schemas, limiting their scalability and domain coverage.
Approach:	They propose a framework for fully autonomous knowledge graph construction that eliminates the need for predefined schemas.
Outcome:	The proposed framework outperforms state-of-the-art models on multi-hop QA tasks and enhances LLM factuality.

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Yuhang Yang, Kai Tang, Chao Ye, Haobo Wang, Qiqi Luo, Jin Guang Zheng, Zhixin Zhang

Dialogue Systems & Conversational Agents

Challenge:	Existing models that assume users to be static, rational agents with fixed preferences fail to capture rich behavioral heterogeneity in real-world debt collection scenarios.
Approach:	They propose a public persona-enriched debt collection benchmark that highlights behavioral heterogeneity in negotiation.
Outcome:	The proposed benchmark outperforms existing models in realistic scenarios using 16 state-of-the-art LLMs.

Knowledge-Centric Hallucination Detection (2024.emnlp-main)

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Xiangkun Hu, Dongyu Ru, Lin Qiu, Qipeng Guo, Tianhang Zhang, Yang Xu, Yun Luo, Pengfei Liu, Yue Zhang, Zheng Zhang

Reasoning

Challenge:	Large Language Models (LLMs) have shown impressive capabilities but a tendency to hallucinate.
Approach:	They propose a framework that introduces claim-triplets to represent claims in LLM responses and evaluates them against a reference.
Outcome:	The proposed framework outperforms prior methods by 18.2 to 27.2 points on a benchmark spanning various NLP tasks and annotated 11k claim-triplets from 2.1k responses by seven LLMs.

Scaling Laws for Code: A More Data-Hungry Regime (2026.acl-long)

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Xianzhen Luo, Wenzhen Zheng, Qingfu Zhu, Rongyi Zhang, Houyi Li, Siming Huang, YuanTao Fan, Wanxiang Che

Challenge:	Code Large Language Models (LLMs) are revolutionizing software engineering, but scaling laws are primarily analyzed on Natural Language (NL).
Approach:	They fit Chinchilla law and Farsser law to test scaling laws for code . they find code is more data-hungry and requires higher data-to-parameter ratio .
Outcome:	The proposed scaling laws show that the more expressive Farsser law offers greater accuracy and scales with model size.

Uncovering Argumentative Flow: A Question-Focus Discourse Structuring Framework (2025.emnlp-main)

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Yini Wang, Xian Zhou, Shengan Zheng, Linpeng Huang, Zhunchen Luo, Wei Luo, Xiaoying Bai

Beyond Examples: Towards Automated Thought-level In-Context Reasoning for Large Language Models (2026.acl-long)

Challenge:	Existing structure modeling approaches fail to capture the author’s rhetorical intent and reasoning process.
Approach:	They propose a Question-Focus discourse structuring framework that explicitly models the underlying argumentative flow by anchoring each argumentative unit to a guiding question and a set of attentional foci.
Outcome:	The proposed framework outperforms baseline models and curated models on an argument reconstruction task in Chinese think-tank articles and claims coverage.

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Jinyang Wu, Mingkuan Feng, Shuai Zhang, Feihu Che, Zhengqi Wen, Chonghua Liao, Ling Yang, Haoran Luo, Zheng Lian, Jianhua Tao

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

Challenge:	In-context learning (ICL) struggles with complex reasoning due to superficial, example-level implicit imitation.
Approach:	They propose an automated method that shifts from surface-level examples to more guidance-oriented thought patterns.
Outcome:	The proposed method achieves 80.6% accuracy on MATH and 62.5% on AMC, surpassing GPT-4o’s 77.2% and 57.5% accuracy.

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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

BIC: Twitter Bot Detection with Text-Graph Interaction and Semantic Consistency (2023.acl-long)

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.

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Zhenyu Lei, Herun Wan, Wenqian Zhang, Shangbin Feng, Zilong Chen, Jundong Li, Qinghua Zheng, Minnan Luo

Large Language Models Are Poor Clinical Decision-Makers: A Comprehensive Benchmark (2024.emnlp-main)

Challenge:	Existing methods to identify bots rely on text or networks alone . text-graph interactions and semantic consistency are essential improvements to combat bot evolution.
Approach:	They propose to combine text-graph interaction and semantic Consistency to model Twitter bots' behavior based on attention weights and a text-graphic interaction module to enable information exchange across modalities in the learning process.
Outcome:	The proposed framework outperforms state-of-the-art methods on two widely adopted datasets and the results are consistent with previous work.

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Fenglin Liu, Zheng Li, Hongjian Zhou, Qingyu Yin, Jingfeng Yang, Xianfeng Tang, Chen Luo, Ming Zeng, Haoming Jiang, Yifan Gao, Priyanka Nigam, Sreyashi Nag, Bing Yin, Yining Hua, Xuan Zhou, Omid Rohanian, Anshul Thakur, Lei Clifton, David Clifton

Counting-Stars: A Multi-evidence, Position-aware, and Scalable Benchmark for Evaluating Long-Context Large Language Models (2025.coling-main)

Challenge:	Existing studies focus on evaluating large language models in close-ended QA tasks, but many clinical decisions involve answering open-ended questions without pre-set options.
Approach:	They construct a benchmark to better understand large language models in the clinic . they use existing datasets to evaluate LLMs in clinical situations .
Outcome:	The proposed model outperforms human experts in multiple medical tasks.

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Mingyang Song, Mao Zheng, Xuan Luo

Lost in Execution: On the Multilingual Robustness of Tool Calling in Large Language Models (2026.acl-long)

Challenge:	Existing benchmarks for long-context language models have lagged behind . however, there is still room for improvement as the context window and complexity of the tasks increase.
Approach:	They propose a long-context benchmark to evaluate the performance of long-text language models.
Outcome:	The proposed benchmarks show that the models perform better in long-context environments as the context window increases and complexity increases.

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Zheng Luo, T Pranav Kutralingam, Ogochukwu N. Okoani, Wanpeng Xu, Hua Wei, Xiyang Hu

Robustness in NLP

Challenge:	Large Language Models (LLMs) are increasingly deployed as agents that invoke external tools through structured function calls.
Approach:	They introduce a diagnostic benchmark and conduct a systematic evaluation of multilingual tool calling across Chinese, Hindi, and the low-resource language Igbo.
Outcome:	The proposed benchmarks show that multilingual tool calling fails despite correct intent understanding and tool selection.

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

Reinforcing Agentic Search Via Reward Density Optimization (2026.acl-long)

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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Kun Luo, Hongjin Qian, Zheng Liu, Ziyi Xia, Shitao Xiao, Zhao Cao, Siqi Bao, Jun Zhao, Kang Liu

DCMKC: A Dual Consistency Matching Approach for Multi-hop Question Answering in LLMs (2025.findings-emnlp)

Challenge:	Reinforcement Learning with Verifiable Rewards (RLVR) is a promising approach for enhancing agentic search, but its performance is often hindered by reward sparsity .
Approach:	They propose a new research problem to improve the reward obtained per unit of exploration cost by using a system that decomposes long-horizon tasks into intermediate objectives and assigns process-level rewards to provide denser learning signals.
Outcome:	The proposed framework outperforms strong baselines on several agentic search benchmarks and achieves comparable performance to that of advanced proprietary models.

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Xinyi Wang, Yiping Song, Chang Liu, Tingjin Luo, Bo Liu, Zheng Xie, Minlie Huang

Structured Data in NLP

Challenge:	Existing reasoning based on chains of thought (CoTs) fails to find logical connections between reasoning steps .
Approach:	They propose a method to match KG reasoning chains with CoTs based on semantic similarity . they use a knowledge graph to find relevant information "within" each reasoning step .
Outcome:	The proposed method outperforms baselines on multi-answer questions with 5.1% improvement over baselines.

A Survey of Reinforcement Learning for Large Language Models under Data Scarcity: Challenges and Solutions (2026.acl-long)

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Zhiyin Yu, Yuchen Mou, Juncheng Yan, Junyu Luo, Chunchun Chen, Xing Wei, Yunhui Liu, Hongru Sun, Yuxing Zhang, Jun Xu, Yatao Bian, Ming Zhang, Wei Ye, Tieke He, Jie Yang, Guanjie Zheng, Zhonghai Wu, Bo Zhang, Lei Bai, Xiao Luo

Grammar-Based Code Representation: Is It a Worthy Pursuit for LLMs? (2025.findings-acl)

Challenge:	Existing research on reinforcement learning for LLMs under data scarcity has not been unified.
Approach:	They propose a top-up hierarchical framework built around three complementary perspectives: data-centric, training-centric and framework-centric.
Outcome:	The proposed framework provides a clear conceptual foundation for understanding the design space of data-efficient RL for large language models and to guide researchers working in this emerging area.

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Qingyuan Liang, Zhao Zhang, Zeyu Sun, Zheng Lin, Qi Luo, Xiao Yueyi, Yizhou Chen, Yuqun Zhang, Haotian Zhang, Lu Zhang, Chenbin Chenbin, Yingfei Xiong

Long Context is Not Long at All: A Prospector of Long-Dependency Data for Large Language Models (2024.acl-long)

Challenge:	Existing research demonstrates the effectiveness of grammar-based code representations in small-scale models, showing their ability to reduce syntax errors and enhance performance.
Approach:	They develop a series of billion-scale grammar-based code representations that incorporate grammar rules into the code generation process.
Outcome:	Experiments on HumanEval and MBPP show that grammar-based representations reduce syntax errors and improve performance even in billion-scale models.

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Longze Chen, Ziqiang Liu, Wanwei He, Yinhe Zheng, Hao Sun, Yunshui Li, Run Luo, Min Yang

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

Challenge:	Long-context modeling capabilities are important for large language models (LLMs) however, training LLMs with long context windows is insufficient since some samples do not exhibit strong semantic dependencies across long contexts.
Approach:	They propose a data mining framework ProLong that assigns each training sample with a long dependency score and ranks and filters them according to their results.
Outcome:	The proposed framework can rank and filter training samples that exhibit more powerful long-context modeling abilities.

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Baixuan Xu, Weiqi Wang, Haochen Shi, Wenxuan Ding, Huihao Jing, Tianqing Fang, Jiaxin Bai, Xin Liu, Changlong Yu, Zheng Li, Chen Luo, Qingyu Yin, Bing Yin, Long Chen, Yangqiu Song

Green & Sustainable NLP

Challenge:	Existing methods for acquiring large-scale intentions generate product-centric intentions without product images and incur high costs for scalability.
Approach:	They propose a multimodal framework that allows Large Vision-Language Models to infer purchase intentions from multimodal product metadata and prioritize human-centric ones.
Outcome:	The proposed framework shows that it is robust to different prompts and superior to previous methods.

How Does the Textual Information Affect the Retrieval of Multimodal In-Context Learning? (2024.emnlp-main)

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Yang Luo, Zangwei Zheng, Zirui Zhu, Yang You

Low-Resource NLP

Challenge:	MLLMs have significant capabilities for multimodal in-context learning, but their effectiveness hinges on the appropriate selection of in-constext examples.
Approach:	They propose a supervised MLLM prompt retriever that leverages a trained retriever based on MLML's confidence to select examples, which enhances multimodal in-context learning efficiency.
Outcome:	The proposed method is validated through extensive testing across three different tasks and demonstrates its effectiveness.

Estimating Soft Labels for Out-of-Domain Intent Detection (2022.emnlp-main)

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Hao Lang, Yinhe Zheng, Jian Sun, Fei Huang, Luo Si, Yongbin Li

Sentiment Analysis

Challenge:	Existing methods to detect out-of-dominance (OOD) intents are limited by the lack of OOD samples.
Approach:	They propose an adaptive soft pseudo labeling method that can estimate soft labels for pseudo OOD samples when training OOD detectors.
Outcome:	The proposed method outperforms competing methods on three benchmark datasets and consistently outperformed previous methods.

Doc-V*: Coarse-to-Fine Interactive Visual Reasoning for Multi-Page Document VQA (2026.acl-long)

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Yuanlei Zheng, Pei Fu, Hang Li, Ziyang Wang, Yuyi Zhang, Wenyu Ruan, Xiaojin Zhang, Zhongyu Wei, Zhenbo Luo, Jian Luan, Wei Chen, Xiang Bai

Dynamic Emotion and Personality Profiling for Multimodal Deception Detection (2026.acl-long)

Challenge:	Existing OCR-free approaches to document visual question answering are brittle and passive.
Approach:	They propose an OCR-free agentic framework that casts multi-page DocVQA as sequential evidence aggregation.
Outcome:	The proposed framework outperforms open-source and proprietary models in five benchmarks and improves out-of-domain performance by 47.9% over baseline.

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Li Zheng, Yanyi Luo, Hao Fei, Yuzhe Ding, Yujie Huang, Fei Li, Chong Teng, Donghong Ji

SPACE-2: Tree-Structured Semi-Supervised Contrastive Pre-training for Task-Oriented Dialog Understanding (2022.coling-1)

Challenge:	Existing methods for deception detection lack sample-level dynamic annotations for emotions and personality.
Approach:	They propose a multi-model multi-prompt annotation scheme and a strict label quality evaluation standard for deception, emotion, and personality annotations.
Outcome:	The proposed framework outperforms state-of-the-art models on the MDPE and DDEP datasets.

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Wanwei He, Yinpei Dai, Binyuan Hui, Min Yang, Zheng Cao, Jianbo Dong, Fei Huang, Luo Si, Yongbin Li

ELBA-Bench: An Efficient Learning Backdoor Attacks Benchmark for Large Language Models (2025.acl-long)

Challenge:	Existing methods for dialog understanding only consider self-augmented dialogs as positive samples and treat all other dialogs like negative ones.
Approach:	They propose a tree-structured pre-trained conversation model which learns dialog representations from limited labeled dialogs and large-scale unlabeled dialog corpora via semi-supervised contrastive pre-training.
Outcome:	The proposed model can achieve state-of-the-art results on the DialoGLUE benchmark.

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Xuxu Liu, Siyuan Liang, Mengya Han, Yong Luo, Aishan Liu, Xiantao Cai, Zheng He, Dacheng Tao

Collision to Cognition: Hash-Driven Graph Construction for Efficient RAG (2026.acl-long)

Challenge:	Existing backdoor models are limited in coverage of attack, system integrity and backdoor alignment . ELBA-Bench provides over 1300 experiments encompassing 12 attack methods, 18 datasets, and 12 LLMs.
Approach:	They propose a framework that allows attackers to inject backdoor through parameter efficient fine-tuning or without fine-uning techniques.
Outcome:	ELBA-Bench provides over 1300 experiments encompassing 12 attack methods, 18 datasets, and 12 LLMs.

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Chuang Zhou, Zheng Yuan, Linhao Luo, Zhaozhuo Xu, Yilin Xiao, Junnan Dong, Siyu An, di Yin, Xing Sun, Xiao Huang

Is Cognition Consistent with Perception? Assessing and Mitigating Multimodal Knowledge Conflicts in Document Understanding (2025.emnlp-main)

Challenge:	Retrieval-augmented generation (RAG) has been used for enhancing large language models with external knowledge.
Approach:	They propose a framework for mining efficient graph structures via hashing to enhance RAG . they adopt an inductive paradigm where global graph structure emerges from local hash collisions .
Outcome:	The proposed framework outperforms existing baselines while requiring no GPU resources or token budget.

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Zirui Shao, Feiyu Gao, Zhaoqing Zhu, Chuwei Luo, Hangdi Xing, Zhi Yu, Qi Zheng, Ming Yan, Jiajun Bu

PAR: Political Actor Representation Learning with Social Context and Expert Knowledge (2022.emnlp-main)

Challenge:	Multimodal large language models (MLLMs) have shown impressive capabilities in document understanding due to different types of annotation noise in training.
Approach:	They propose a method to reduce C&P knowledge conflicts across all tested MLLMs . they propose to use annotation noise to train models to understand document content .
Outcome:	The proposed method reduces C&P knowledge conflicts across all tested MLLMs and enhances their performance in both cognitive and perceptual tasks.

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Shangbin Feng, Zhaoxuan Tan, Zilong Chen, Ningnan Wang, Peisheng Yu, Qinghua Zheng, Xiaojun Chang, Minnan Luo

Representation Learning

Challenge:	Existing approaches focus on textual data and voting records to induce political actors' stances.
Approach:	They propose a Political Actor Representation learning framework that leverages social context and expert knowledge to model ideological stances.
Outcome:	The proposed framework improves political text understanding and improves roll call vote prediction and political perspective detection.

IntentionQA: A Benchmark for Evaluating Purchase Intention Comprehension Abilities of Language Models in E-commerce (2024.findings-emnlp)

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Wenxuan Ding, Weiqi Wang, Sze Kwok, Minghao Liu, Tianqing Fang, Jiaxin Bai, Xin Liu, Changlong Yu, Zheng Li, Chen Luo, Qingyu Yin, Bing Yin, Junxian He, Yangqiu Song

mPLUG: Effective and Efficient Vision-Language Learning by Cross-modal Skip-connections (2022.emnlp-main)

Challenge:	Existing approaches that distill intentions from LMs fail to generate meaningful and human-centric intentions applicable in real-world E-commerce contexts.
Approach:	They propose a double-task multiple-choice question answering benchmark to evaluate LMs' comprehension of purchase intentions in E-commerce.
Outcome:	The proposed benchmark consists of 4,360 carefully curated problems across three difficulty levels, constructed using an automated pipeline to ensure scalability on large E-commerce platforms.

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Chenliang Li, Haiyang Xu, Junfeng Tian, Wei Wang, Ming Yan, Bin Bi, Jiabo Ye, He Chen, Guohai Xu, Zheng Cao, Ji Zhang, Songfang Huang, Fei Huang, Jingren Zhou, Luo Si

Visual Data in NLP

Challenge:	Existing pre-trained vision-language models suffer from inefficiency and linguistic signal overwhelmed by long visual sequences in cross-modal alignment.
Approach:	They propose a vision-language foundation model with cross-modal skip-connections that can be pre-trained end-to-end on large-scale image-text pairs with both discriminative and generative objectives.
Outcome:	The proposed model achieves state-of-the-art results on a wide range of vision-language downstream tasks, including image captioning, image-text retrieval, visual grounding and visual question answering.

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

CAME: Confidence-guided Adaptive Memory Efficient Optimization (2023.acl-long)

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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Yang Luo, Xiaozhe Ren, Zangwei Zheng, Zhuo Jiang, Xin Jiang, Yang You

NiuTrans.LMT: Toward Inclusive and Scalable Multilingual Machine Translation with LLMs (2026.acl-long)

Challenge:	Existing memory-efficient methods require second-moment estimates of the per-parameter gradients to maintain their performance.
Approach:	They propose to use memory-efficient optimizers to reduce memory usage by preserving second-moment estimates of gradients.
Outcome:	The proposed method achieves fast convergence and lower memory usage across training tasks.

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Yingfeng Luo, Ziqiang Xu, Yuxuan Ouyang, MuRun Yang, DingYang Lin, Kaiyan Chang, Tong Zheng, Bei Li, Peinan Feng, Quan Du, Tong Xiao, JingBo Zhu

Multilinguality

Challenge:	Large language models have significantly advanced Multilingual Machine Translation (MMT) yet scaling to many languages while maintaining robust performance across directions remains challenging.
Approach:	They propose a strategy to reduce the number of translations in one direction . they propose auxiliary parallel sentences to promote cross-lingual transfer .
Outcome:	The proposed model performs on par with or better than substantially larger baselines.

Benchmarking Egocentric Clinical Intent Understanding Capability for Medical Multimodal Large Language Models (2026.acl-long)

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Shaonan Liu, Guo Yu, Xiaoling Luo, Shiyi Zheng, Jie Liu, Wenting Chen, Linlin Shen

PerSphere: A Comprehensive Framework for Multi-Faceted Perspective Retrieval and Summarization (2025.acl-long)

Challenge:	Existing benchmarks fail to evaluate egocentric clinical intent understanding of medical multimodal large language models.
Approach:	They propose a benchmark leveraging clinician gaze as a Cognitive Cursor to assess intent understanding across surgery, emergency simulation and diagnostic interpretation.
Outcome:	The proposed benchmark addresses challenges of visual homogeneity of anatomical structures, strict temporal-causal dependencies in clinical workflows, and implicit adherence to safety protocols.

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Yun Luo, Yingjie Li, Xiangkun Hu, Qinglin Qi, Fang Guo, Qipeng Guo, Zheng Zhang, Yue Zhang

Information Extraction & Text Mining

Challenge:	Experimental results show that the main challenge lies in long context and perspective extraction.
Approach:	They propose a benchmark to facilitate multi-faceted perspective retrieval and summarization . they propose measurable metrics to evaluate the comprehensiveness of the retrieval pipeline .
Outcome:	The proposed system breaks free from information silos by combining two opposing claims . it can be used to extract multiple perspectives and improve performance on the platform .

CBLUE: A Chinese Biomedical Language Understanding Evaluation Benchmark (2022.acl-long)

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Ningyu Zhang, Mosha Chen, Zhen Bi, Xiaozhuan Liang, Lei Li, Xin Shang, Kangping Yin, Chuanqi Tan, Jian Xu, Fei Huang, Luo Si, Yuan Ni, Guotong Xie, Zhifang Sui, Baobao Chang, Hui Zong, Zheng Yuan, Linfeng Li, Jun Yan, Hongying Zan, Kunli Zhang, Buzhou Tang, Qingcai Chen

Challenge:	a new benchmark for biomedical language understanding is being developed in Chinese . most benchmarks are limited to English, which makes it difficult to replicate success in other languages.
Approach:	They propose to use Chinese biomedical language understanding evaluation benchmarks to evaluate Chinese models.
Outcome:	The proposed benchmarks show that the current models perform worse than the human ceiling.

Large Language Models as Foundations for Next-Gen Dense Retrieval: A Comprehensive Empirical Assessment (2024.emnlp-main)

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Kun Luo, Minghao Qin, Zheng Liu, Shitao Xiao, Jun Zhao, Kang Liu

SessionIntentBench: A Multi-task Inter-session Intention-shift Modeling Benchmark for E-commerce Customer Behavior Understanding (2026.findings-acl)

Challenge:	Pre-trained language models have limited generalization capabilities and performance challenges.
Approach:	They evaluate 15 different backbone LLMs and non-LLMs to evaluate their performance . larger models and extensive pre-training consistently enhance in-domain accuracy and data efficiency .
Outcome:	The results show that larger models and extensive pre-training enhance in-domain accuracy and data efficiency.

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Yuqi Yang, Weiqi Wang, Baixuan Xu, Wei Fan, Qing Zong, Chunkit Chan, Zheye Deng, Xin Liu, Yifan Gao, Changlong Yu, Chen Luo, Yang Li, Zheng Li, Qingyu Yin, Bing Yin, Yangqiu Song

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

Challenge:	Existing models fail to capture and model customer intention effectively because of insufficient information exploitation and only apparent information like descriptions and titles are used.
Approach:	They propose to exploit existing session data to capture and model intention in E-commerce product purchase sessions using a multimodal benchmark.
Outcome:	The proposed framework can bridge the gap between intention understanding in simplified research cases like co-buy intention and more complex yet practical scenarios like session history.

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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.

Geneverse: A Collection of Open-source Multimodal Large Language Models for Genomic and Proteomic Research (2024.findings-emnlp)

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Tianyu Liu, Yijia Xiao, Xiao Luo, Hua Xu, Wenjin Zheng, Hongyu Zhao

scRAG: Hybrid Retrieval-Augmented Generation for LLM-based Cross-Tissue Single-Cell Annotation (2025.findings-acl)

Challenge:	generative Large Language Models (LLMs) are a promising tool for biomedical and healthcare research.
Approach:	They propose to use finetuned LLMs and multimodal LLM for genomic and proteomics tasks.
Outcome:	The proposed models outperform closed-source models in genomic and proteomics tasks and are highly accurate.

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Zhiyin Yu, Chao Zheng, Chong Chen, Xian-Sheng Hua, Xiao Luo

Taking a Deep Breath: Enhancing Language Modeling of Large Language Models with Sentinel Tokens (2024.findings-emnlp)

Challenge:	Large language models (LLMs) have demonstrated impressive potential in a wide range of fields, including biology, genomics and healthcare.
Approach:	They propose a framework that integrates advanced LLM-based RAG techniques into cross-tissue single-cell annotation.
Outcome:	The proposed framework outperforms baseline models, generalist models, domain-specific methods, and trained classifiers on a cross-tissue dataset.

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Weiyao Luo, Suncong Zheng, Heming Xia, Weikang Wang, Yan Lei, Tianyu Liu, Shuang Chen, Zhifang Sui