Papers by Yuan Luo

AMANDA: Agentic Medical Knowledge Augmentation for Data-Efficient Medical Visual Question Answering (2025.findings-emnlp)

Challenge:	Recent training-based TTS methods, such as continued reinforcement learning, have surged in popularity, while training-free TTS approaches are gradually fading from prominence.
Approach:	They propose a fine-grained sequential scaling method guided by process verification that integrates training-free TTS methods with other classical parallel scaling methods at the step level.
Outcome:	Experiments on five instruction-tuned large language models (LLMs) show that training-free TTS methods can extend reasoning performance boundaries.

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Ziqing Wang, Chengsheng Mao, Xiaole Wen, Yuan Luo, Kaize Ding

Improving Medical NLI Using Context-Aware Domain Knowledge (2020.starsem-1)

Challenge:	Existing Med-MLLMs fail when deployed in low-resource settings where abundant labeled data is unavailable.
Approach:	They propose a training-free agentic framework that performs medical knowledge augmentation via LLM agents.
Outcome:	The proposed framework performs medical knowledge augmentation via LLM agents.

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Shaika Chowdhury, Philip Yu, Yuan Luo

Knowledge Representation

Challenge:	Domain knowledge is important to understand both the lexical and relational associations of words in natural language text . lack of annotated dataset can lead to wrong inference predictions .
Approach:	They propose a knowledge adaptive approach that encodes the premise/hypothesis texts by leveraging supplementary external knowledge alongside the UMLS based on the word contexts.
Outcome:	The proposed model can align token-level interactions between the premise and hypothesis more effectively.

Unity in Diversity: Collaborative Pre-training Across Multimodal Medical Sources (2024.acl-long)

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Xiaochen Wang, Junyu Luo, Jiaqi Wang, Yuan Zhong, Xiaokun Zhang, Yaqing Wang, Parminder Bhatia, Cao Xiao, Fenglong Ma

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

Challenge:	Current pre-training techniques rely on a limited scope of medical data, limiting the range of downstream tasks.
Approach:	They propose a pre-training strategy that unifies patient data within individual sources and captures explicit and implicit correlations between patients across different sources.
Outcome:	The proposed strategy bridges the gap between multimodal medical sources by aggregating patient data within individual sources and capturing explicit and implicit correlations between patients across sources.

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

A Survey on Efficient Large Language Model Training: From Data-centric Perspectives (2025.acl-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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Junyu Luo, Bohan Wu, Xiao Luo, Zhiping Xiao, Yiqiao Jin, Rong-Cheng Tu, Nan Yin, Yifan Wang, Jingyang Yuan, Wei Ju, Ming Zhang

QBridge: Bridging Natural Language and SQL via Gold Query Rewriting with Agentic Refinement (2026.acl-long)

Challenge:	achieving data-efficient post-training of Large Language Models is a key research question.
Approach:	They propose a taxonomy of data-efficient LLM post-training methods from a data-centric perspective.
Outcome:	The proposed methods cover data selection, data quality enhancement, synthetic data generation, data distillation and compression, and self-evolving data ecosystems.

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Zhensheng Luo, Sai Wu, Yuan Qiu, Chang Yao, Gang Chen, Xiu Tang

Programming Languages in NLP

Challenge:	Natural language to SQL (NL2SQL) is an intuitive interface for querying structured data . but real user questions are noisy, ambiguous, and weakly grounded to database semantics.
Approach:	They propose an agentic feedback-driven NL2SQL framework that bridges natural language and SQL via Gold Query.
Outcome:	The proposed framework outperforms strong prompting and agentic baselines on spider, BIRD, and three robustness variants on NL2SQL.

LJPCheck: Functional Tests for Legal Judgment Prediction (2024.findings-acl)

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Yuan Zhang, Wanhong Huang, Yi Feng, Chuanyi Li, Zhiwei Fei, Jidong Ge, Bin Luo, Vincent Ng

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

Challenge:	Existing LJP models fail to evaluate specific aspects of their performance, such as legal fairness and judicial fairness.
Approach:	They propose a suite of functional tests for LJP models to comprehend LJp models’ behaviors and offer diagnostic insights.
Outcome:	Extensive tests reveal weaknesses in LJP models and provide diagnostic insights.

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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

Information Retrieval

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.

EMPEC: A Comprehensive Benchmark for Evaluating Large Language Models Across Diverse Healthcare Professions (2025.findings-acl)

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Zheheng Luo, Chenhan Yuan, Qianqian Xie, Sophia Ananiadou

Search Augmented Instruction Learning (2023.findings-emnlp)

Challenge:	Recent advances in Large Language Models (LLMs) show their potential in accurately answering biomedical questions, yet current healthcare benchmarks primarily assess knowledge mastered by medical doctors, neglecting other essential professions.
Approach:	They evaluated 17 LLMs including proprietary and open-source models and found they struggled with specialized fields and alternative medicine.
Outcome:	The examinations for medical PErsonnel in Chinese (EMPEC) features 157,803 exam questions across 124 subjects and 20 healthcare professions.

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Hongyin Luo, Tianhua Zhang, Yung-Sung Chuang, Yuan Gong, Yoon Kim, Xixin Wu, Helen Meng, James Glass

Compressing then Matching: An Efficient Pre-training Paradigm for Multimodal Embedding (2026.acl-long)

Challenge:	Large language models (LLMs) have been significantly improved by instruction fine-tuning, but still lack transparency and the ability to utilize up-to-date knowledge and information.
Approach:	They propose a search-augmented instruction learning model which grounds the language generation and instruction following abilities on complex search results generated by in-house and external search engines.
Outcome:	The proposed model outperforms plain LLMs on zero-shot language tasks and can generate both natural and programming languages following natural language guidance and requests.

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Da Li, Yuxiao Luo, Keping Bi, Jiafeng Guo, Wei Yuan, Biao Yang, Yan Wang, Fan Yang, Tingting Gao, Guorui Zhou

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

Challenge:	Recent approaches demonstrate that MLLMs can be adapted into competitive embedding models via large-scale contrastive learning.
Approach:	They propose a compressed pre-training phase which serves as a warm-up stage for contrastive learning.
Outcome:	The proposed model achieves state-of-the-art among MLLMs of comparable size on the MMEB, realizing optimization in both efficiency and effectiveness.

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

Towards IP Intelligence: Benchmarking Large Language Models on Intellectual Property Knowledge and Practice (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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Qiyao Wang, Guhong Chen, Hongbo Wang, Huaren Liu, Minghui Zhu, Zhifei Qin, Li Linwei, Yilin Yue, Shiqiang Wang, Jiayan Li, Wu Yihang, Ziqiang Liu, Longze Chen, Run Luo, Liyang Fan, Jiaming Li, Lei Zhang, Kan Xu, Hamid Alinejad-Rokny, Chengming Li, Shiwen Ni, Yuan Lin, Min Yang

QFinZero: A Unified Financial Toolchain for LLM-Based Trading Agents (2026.acl-demo)

Challenge:	Existing datasets and benchmarks focus only on patents or cover limited aspects of the IP field, lacking alignment with real-world scenarios.
Approach:	They propose a bilingual IP task taxonomy and a large-scale bilingual benchmark to evaluate LLMs in real-world IP practice.
Outcome:	The proposed model achieves only 75.8% accuracy, indicating room for improvement . open-source IP and law-oriented models lag behind closed-source general-purpose models .

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Haochen Luo, Yifan LI, Ho Tin Ko, An Binh Minh, Junjie Xu, Tang Pok Hin, Wang Chak Wong, Gao Yuan, Zhengzhao Lai, Yuan Zhang, Chen Liu

Information Retrieval

Challenge:	Existing trading systems rely on fragmented and task-specific APIs, resulting in inconsistent schemas and limited reproducibility.
Approach:	They propose a unified trading environment for large language model (LLM) agents that standardizes three core capabilities . they argue that such a standardized trading environment is essential for scalable research on LLM-based financial agents.
Outcome:	The proposed trading environment reduces engineering overhead and supports reproducible evaluation through comprehensive logging and deterministic replay.

From Mimesis to Metamorphosis: Evolving VLM Judges via In-Context Comparing and Knowledge Internalization (2026.findings-acl)

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Juntuo Wang, Yuming Qiao, Yifan Yang, Lunxi Yuan, Liang Luo, Dan Meng

Information Retrieval

Challenge:	Existing approaches to subjective assessment are inconsistent and inconsistent due to inconsistent scales and inherent preference biases.
Approach:	They propose a framework that operationalizes subjective assessment as comparative analysis and internalizes it via Language Buttons.
Outcome:	The proposed framework achieves state-of-the-art performance across multiple benchmarks and is scale-steerable.

Autonomous Data Selection with Zero-shot Generative Classifiers for Mathematical Texts (2025.findings-acl)

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Yifan Zhang, Yifan Luo, Yang Yuan, Andrew C Yao

Numerical Reasoning

Challenge:	Existing methods that require human annotations or training a dedicated data filter to curate high-quality mathematical texts are based on autonomous data selection.
Approach:	They propose a method that leverages base language models as zero-shot "generative classifiers" they use a model's logits to determine whether a given passage is mathematically informative and educational .
Outcome:	The proposed method significantly boosts downstream performance on math benchmarks while using far fewer tokens than previous methods.

Explicit Alignment and Many-to-many Entailment Based Reasoning for Conversational Machine Reading (2023.findings-emnlp)

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Yangyang Luo, Shiyu Tian, Caixia Yuan, Xiaojie Wang

Natural Language Interfaces

Challenge:	Recent research has explored how to improve the abilities of decision-making and question generation.
Approach:	They propose a pipeline framework that aligns the document and user-provided information in an explicit way, makes decisions using a lightweight many-to-many entailment reasoning module and generates follow-up questions based on the document.
Outcome:	The proposed framework achieves state-of-the-art in micro-accuracy and ranks the first place on the public leaderboard of the CMR benchmark dataset ShARC.

Natural Language Embedded Programs for Hybrid Language Symbolic Reasoning (2024.findings-naacl)

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Tianhua Zhang, Jiaxin Ge, Hongyin Luo, Yung-Sung Chuang, Mingye Gao, Yuan Gong, Yoon Kim, Xixin Wu, Helen Meng, James Glass

MARK: Multi-agent Collaboration with Ranking Guidance for Text-attributed Graph Clustering (2025.findings-acl)

Challenge:	Existing methods for surfacing symbolic reasoning capabilities are limited to narrow tasks . arithmetic computations are unnatural to perform in pure language space, and hence present difficulties for LLMs.
Approach:	They propose a natural language embedded program framework for solving symbolic reasoning tasks.
Outcome:	The proposed framework improves on strong baselines across math and symbolic reasoning, text classification, question answering, and instruction following tasks.

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Yiwei Fu, Yuxing Zhang, Chunchun Chen, JianwenMa JianwenMa, Quan Yuan, Rong-Cheng Tu, Xinli Huang, Wei Ye, Xiao Luo, Minghua Deng

Multimodality

Challenge:	Existing approaches to cluster graphs with GNNs are limited due to label scarcity.
Approach:	They propose to leverage large language models to enhance text-attributed graph clustering by using three LLMs as ranking-based supervision signals.
Outcome:	The proposed approach generates reliable guidance using collaboration of three LLM-based agents as ranking-based supervision signals.

SciCustom: A Framework for Custom Evaluation of Scientific Capabilities in Large Language Models (2026.acl-long)

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Yiyang Gu, Junwei Yang, Junyu Luo, Ye Yuan, Bin Feng, Yingce Xia, Shufang Xie, Kaili Liu, Bohan Wu, Qi Shi, Haoran Li, Beier Xiao, Zhiping Xiao, Xiao Luo, Weizhi Zhang, Philip S. Yu, Zequn Liu, Ming Zhang

ART: Attention Replacement Technique to Improve Factuality in LLMs (2026.acl-long)

Challenge:	Existing evaluations of large language models fail to reflect fine-grained capabilities . existing benchmarks are manually curated or domain-generic, limiting scalability and alignment with real use cases.
Approach:	They propose a framework that allows custom construction of benchmarks from large-scale scientific data to evaluate application-specific scientific capabilities in LLMs.
Outcome:	The proposed framework reveals fine-grained differences in scientific capabilities that standard benchmarks overlook . it allows custom construction of benchmarks from large-scale scientific data to evaluate application-specific capabilities in LLMs.

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Ziqin Luo, Yihao Quan, Xiaofeng Zhang, Xiaosong Yuan, Chen Shen

Reasoning

Challenge:	Existing methods to mitigate hallucinations in large language models are expensive and require significant resources.
Approach:	They propose a training-free method that replaces uniform attention patterns in shallow layers with local attention patterns to reduce hallucinations.
Outcome:	The proposed method reduces hallucinations across multiple LLM architectures.

DeKeyNLU: Enhancing Natural Language to SQL Generation through Task Decomposition and Keyword Extraction (2025.findings-emnlp)

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Jian Chen, Zhenyan Chen, Xuming Hu, Peilin Zhou, Yining Hua, Han Fang, Cissy Hing Yee Choy, Xinmei Ke, Jingfeng Luo, Zixuan Yuan

Programming Languages in NLP

Challenge:	NL2SQL provides a model-centric paradigm that simplifies database access for non-technical users . challenges such as inaccurate task decomposition and keyword extraction remain major bottlenecks .
Approach:	They propose a RAG-based NL2SQL pipeline that employs three modules for query understanding, entity retrieval, and generation to improve SQL generation accuracy.
Outcome:	The proposed pipeline improves the accuracy of query generation on BIRD and Spider datasets.

Act as you think: Reinforcing Consistent Reasoning in Medical Visual Question Answering (2026.acl-long)

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Songtao Jiang, Yuan Wang, Ruizhe Chen, Yan Zhang, Ruilin Luo, Bohan Lei, Yeying Jin, Sibo Song, ZhiBo Yang, Jimeng Sun, Jian Wu, Zuozhu Liu

Robustness in NLP

Challenge:	Recent advances have improved the accuracy of medical visual question answering (Med-VQA) however, the high stakes nature of the medical domain has precipitated a shift towards interpretability and transparency of reasoning processes.
Approach:	They propose a reinforcement learning from verifiable rewards framework that rewards internal consistency and logical coherence.
Outcome:	The proposed framework rewards internal consistency and logical coherence, and is highly versatile, the authors show.

Multilingual Retrieval Augmented Generation for Culturally-Sensitive Tasks: A Benchmark for Cross-lingual Robustness (2025.findings-acl)

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Bryan Li, Fiona Luo, Samar Haider, Adwait Agashe, Siyu Li, Runqi Liu, Miranda Muqing Miao, Shriya Ramakrishnan, Yuan Yuan, Chris Callison-Burch

Multilinguality

Challenge:	Existing approaches to retrieval-augmented generated (RAG) can be useful in multilingual settings, but they also introduce biases in the retrieved documents.
Approach:	They propose a dataset of territorial disputes paired with retrieved Wikipedia documents in 49 languages to evaluate cross-lingual robustness.
Outcome:	The proposed paradigm helps mitigate hallucinations of large language models (LLMs).

Dirichlet Latent Variable Hierarchical Recurrent Encoder-Decoder in Dialogue Generation (D19-1)

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Min Zeng, Yisen Wang, Yuan Luo

Dialogue Systems & Conversational Agents

Challenge:	Existing work assumes the Gaussian priors of the latent variable, which are incapable of representing complex latent variables effectively.
Approach:	They propose to use the Dirichlet distribution with flexible structures to characterize latent variables in place of the Gaussian priors.
Outcome:	The proposed model outperforms existing models on the dialogue generation task.

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

KMatrix-2: A Comprehensive Heterogeneous Knowledge Collaborative Enhancement Toolkit for Large Language Model (2025.emnlp-demos)

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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Shun Wu, Di Wu, Wangtao Sun, Ziyang Huang, Xiaowei Yuan, Kun Luo, XueYou Zhang, Shizhu He, Jun Zhao, Kang Liu

On the In-context Generation of Language Models (2024.emnlp-main)

Challenge:	Existing studies on K-LLMs systems focus on declarative knowledge and procedural knowledge (rules) .
Approach:	They propose to build a toolkit that supports comprehensive heterogeneous knowledge collaborative enhancement for Large Language Models (LLMs).
Outcome:	The proposed toolkit provides unified knowledge integration and joint knowledge retrieval methods to achieve more comprehensive heterogeneous knowledge collaborative enhancement.

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Zhongtao Jiang, Yuanzhe Zhang, Kun Luo, Xiaowei Yuan, Jun Zhao, Kang Liu

NaviMaster: Learning a Unified Policy for GUI and Embodied Navigation Tasks (2026.acl-long)

Challenge:	Large language models (LLMs) have the ability of in-context generation (ICG) when given an in-text prompt, they can implicitly recognize the pattern of the examples and complete the prompt in the desired way.
Approach:	They propose a plausible latent variable model to model the distribution of pretrained corpora and formalize ICG as a problem of next topic prediction.
Outcome:	The proposed model can model the distribution of pretrained corpora and then formalize ICG as a problem of next topic prediction.

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Zhihao Luo, Wentao Yan, Jingyu Gong, Min Wang, Zhizhong Zhang, Xuhong Wang, Yuan Xie, Xin Tan

Challenge:	Recent advances in Graphical User Interface (GUI) and embodied navigation have driven progress, yet these domains have largely evolved in isolation, with disparate datasets and training paradigms.
Approach:	They propose a visual-target trajectory collection pipeline that generates trajectories for GUI and embodied tasks using a single formulation.
Outcome:	The proposed agent outperforms state-of-the-art agents in GUI navigation, spatial affordance prediction, and embodied navigation.

Shorten After You’re Right: Lazy Length Penalties for Reasoning RL (2026.findings-acl)

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Danlong Yuan, Tian Xie, Shaohan Huang, Huishuai Zhang, Zhuocheng Gong, Chong Luo, Furu Wei, Dongyan Zhao

Reasoning

Challenge:	Existing shortening methods for long reasoning models rely on additional supervision or multi-stage post-training.
Approach:	They propose a lazy length penalty that imposes length pressure on models without extra training stages.
Outcome:	The proposed method significantly reduces response length without extra training stages while maintaining or improving performance.

KG-Adapter: Enabling Knowledge Graph Integration in Large Language Models through Parameter-Efficient Fine-Tuning (2024.findings-acl)

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Shiyu Tian, Yangyang Luo, Tianze Xu, Caixia Yuan, Huixing Jiang, Chen Wei, Xiaojie Wang

Native Sparse Attention: Hardware-Aligned and Natively Trainable Sparse Attention (2025.acl-long)

Challenge:	Large language models (LLMs) are criticized for lack of expertise and knowledge conflict . KG-Adapter is a parameter-level KG integration method for decoder-only LLMs .
Approach:	They propose a parameter-level KG integration method based on parameter-efficient fine-tuning . they use KG-Adapter to integrate knowledge graphs with LLMs and perform joint reasoning .
Outcome:	The proposed method outperforms the current state-of-the-art method on four datasets for two different tasks.

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Jingyang Yuan, Huazuo Gao, Damai Dai, Junyu Luo, Liang Zhao, Zhengyan Zhang, Zhenda Xie, Yuxing Wei, Lean Wang, Zhiping Xiao, Yuqing Wang, Chong Ruan, Ming Zhang, Wenfeng Liang, Wangding Zeng

Green & Sustainable NLP

Challenge:	Long-context modeling is crucial for next-generation language models, but high computational cost of standard attention mechanisms poses significant computational challenges.
Approach:	They propose a natively trained Sparse Attention mechanism that integrates algorithms with hardware-aligned optimizations to achieve efficient long-context modeling.
Outcome:	The proposed model maintains or exceeds Full Attention models across general benchmarks, long-context tasks, and instruction-based reasoning.

CENTAUR: Bridging the Impossible Trinity of Privacy, Efficiency, and Performance in Privacy-Preserving Transformer Inference (2025.acl-long)

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Jinglong Luo, Guanzhong Chen, Yehong Zhang, Shiyu Liu, Hui Wang, Yue Yu, Xun Zhou, Yuan Qi, Zenglin Xu

Reverse Constitutional AI: A Framework for Controllable Toxic Data Generation via Probability-Clamped RLAIF (2026.findings-acl)

Challenge:	Existing privacy-preserving Transformer Inference frameworks suffer from high computational overhead and performance losses.
Approach:	They propose a framework that integrates random permutations and SMPC to address the "impossible trinity" CENTAUR resists diverse data reconstruction attacks and boosts inference speed by 5.030.4 times .
Outcome:	CENTAUR achieves an unprecedented balance between privacy, efficiency, and performance.

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Yuan Fang, Yiming Luo, Aimin Zhou, Fei Tan

Robustness in NLP

Challenge:	Existing approaches to red teaming focus on searching for individual adversarial inputs.
Approach:	They propose a framework for automated adversarial data generation that inverts harmless constitution into constitution of toxicity and iteratively refining model outputs through critique–revision pipeline.
Outcome:	The proposed framework generates diverse, high-quality toxic data without human annotation and significantly improves semantic coherence without sacrificing adversarial strength.

Deciphering Undersegmented Ancient Scripts Using Phonetic Prior (2021.tacl-1)

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Jiaming Luo, Frederik Hartmann, Enrico Santus, Regina Barzilay, Yuan Cao

Syntactic Text Processing

Challenge:	a number of undeciphered languages are still undecipherated, igniting fierce scientific debate . a recent study shows that NLP methods can successfully decipher lost languages .
Approach:	They propose a decipherment model that incorporates phonetic geometry into word segmentation and cognate alignment . they use the International Phonetic Alphabet to learn character embeddings based on historical sound change .
Outcome:	The proposed model shows that it can decipher both deciphered and undeciphered languages.

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

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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

ELAINE-medLLM: Lightweight English Japanese Chinese Trilingual Large Language Model for Bio-medical Domain (2025.coling-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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Ken Yano, Zheheng Luo, Jimin Huang, Qianqian Xie, Masaki Asada, Chenhan Yuan, Kailai Yang, Makoto Miwa, Sophia Ananiadou, Jun’ichi Tsujii

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

Challenge:	Existing bilingual or multilingual medical LLMs are limited in multilingual data and therefore perform poorly in non-English languages such as Japanese and Chinese.
Approach:	They propose to use a trilingual (English, Japanese, Chinese) large language model adapted for the bio-medical domain to harness the knowledge and abilities of the base model.
Outcome:	The proposed model can support English, Japanese, and Chinese and is adapted for a bio-medical domain.

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

AutoL2S: Auto Long-Short Reasoning for Efficient Large Language Models (2026.findings-acl)

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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Feng Luo, Yu-Neng Chuang, Guanchu Wang, Hoang Anh Duy Le, Shaochen Zhong, Hongyi Liu, Jiayi Yuan, Yang Sui, Vladimir Braverman, Vipin Chaudhary, Xia Hu

SolEval: Benchmarking Large Language Models for Repository-level Solidity Smart Contract Generation (2025.emnlp-main)

Challenge:	Existing approaches to distilling large language models (LLMs) are inefficient and generate excessively long chain-of-thought reasoning even for inputs that admit concise solutions.
Approach:	They propose a distillation framework that empowers non-reasoning LLMs to think only when necessary.
Outcome:	The proposed framework reduces reasoning length up to 71% with minimal accuracy loss while preserving accuracy.

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Zhiyuan Peng, Xin Yin, Rui Qian, Peiqin Lin, YongKang Liu, Hao Zhang, Chenhao Ying, Yuan Luo

CryptoTrade: A Reflective LLM-based Agent to Guide Zero-shot Cryptocurrency Trading (2024.emnlp-main)

Challenge:	Existing methods focus on Python and Java, neglecting Solidity, the programming language for Ethereum smart contracts.
Approach:	They construct a repository-level benchmark for Solidity to evaluate the performance of LLMs on Ethereum.
Outcome:	The proposed benchmarks show that the best performing LLM achieves only 26.29% Pass@10, highlighting room for improvement in Solidity code generation.

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Yuan Li, Bingqiao Luo, Qian Wang, Nuo Chen, Xu Liu, Bingsheng He

GlotEval: A Test Suite for Massively Multilingual Evaluation of Large Language Models (2025.emnlp-demos)

Challenge:	Large Language Models (LLMs) have been used for financial decision-making and stock market prediction for years.
Approach:	They propose to use Large Language Models to analyze on-chain and off-chain data to provide a comprehensive overview of the cryptocurrency market.
Outcome:	The proposed trading agent leverages the transparency and immutability of on-chain data, as well as the timeliness and influence of off-chain signals, providing a comprehensive overview of the cryptocurrency market.

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Hengyu Luo, Zihao Li, Joseph Attieh, Sawal Devkota, Ona de Gibert, Xu Huang, Shaoxiong Ji, Peiqin Lin, Bhavani Sai Praneeth Varma Mantina, Ananda Sreenidhi, Raúl Vázquez, Mengjie Wang, Samea Yusofi, Fei Yuan, Jörg Tiedemann

Multilinguality

Challenge:	Existing evaluation frameworks focus on English and a handful of high-resource languages, thereby overlooking the realistic performance of large language models in multilingual and lower-resourced scenarios.
Approach:	They propose a unified and lightweight framework that integrates 27 benchmarks under a standard ISO 639-3 language identifier system to enable seamless incorporation of new benchmarks.
Outcome:	The proposed framework integrates 27 benchmarks under a standard ISO 639-3 language identifier system, allowing for seamless incorporation of new benchmarks.

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

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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.

MAXS: Meta-Adaptive Exploration with LLM Agents (2026.findings-acl)

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Jian Zhang, Zhiyuan Wang, Zhangqi Wang, Yu He, Haoran Luo, li Yuan, Lingling Zhang, Rui Mao, Qika Lin, Jun Liu

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

Challenge:	Existing methods for inference are often myopic and have divergent reasoning paths . a meta-adaptive reasoning framework is proposed to improve the efficiency of LLM agents .
Approach:	They propose a meta-adaptive reasoning framework that integrates tool execution and reasoning planning.
Outcome:	The proposed framework outperforms existing methods in performance and inference efficiency.

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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.

Hierarchical Pretraining on Multimodal Electronic Health Records (2023.emnlp-main)

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Xiaochen Wang, Junyu Luo, Jiaqi Wang, Ziyi Yin, Suhan Cui, Yuan Zhong, Yaqing Wang, Fenglong Ma

Dynamic Guided and Domain Applicable Safeguards for Enhanced Security in Large Language Models (2025.findings-naacl)

Challenge:	Existing pretraining models on EHR data are too specific, limiting their transferability.
Approach:	They propose a general, unified pretraining framework for hierarchically multimodal EHR data that can be used to train models on a large dataset before fine-tuning it on 'upstream' tasks.
Outcome:	The proposed model performs on eight downstream tasks spanning three levels and compares with baselines on 18 different tasks.

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Weidi Luo, He Cao, Zijing Liu, Yu Wang, Aidan Wong, Bin Feng, Yuan Yao, Yu Li

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

Challenge:	Existing defense methods struggle with two key issues: inadequate defense capabilities and over-defensiveness.
Approach:	They propose a multi-agents-based framework that leverages accurate external information to provide an unbiased summary of user intentions and safety response guidance.
Outcome:	Experiments on popular jailbreak attacks and benign datasets show that the proposed framework can enhance LLM's robustness against jailbreaks without compromising its general functionality.

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

Multimodality

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.

Neural Decipherment via Minimum-Cost Flow: From Ugaritic to Linear B (P19-1)

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Jiaming Luo, Yuan Cao, Regina Barzilay

Challenge:	Existing methods for decipherment of lost languages are limited by limited data and scarce quantities of ancient text.
Approach:	They propose a neural approach for automatic decipherment of lost languages . they use an expressive sequence-to-sequence model to capture character-level correspondences between cognates .
Outcome:	The proposed approach improves on the decipherment of Ugaritic and Linear B in ancient Greek . the proposed approach is highly customized for a given language pair and does not generalize to other lost languages.

Fine-Grained Image-Text Alignment in Medical Imaging Enables Explainable Cyclic Image-Report Generation (2024.acl-long)

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Wenting Chen, Linlin Shen, Jingyang Lin, Jiebo Luo, Xiang Li, Yixuan Yuan