Papers by Ran Xu
ScaleLLM: A Resource-Frugal LLM Serving Framework by Optimizing End-to-End Efficiency (2024.emnlp-industry)
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Yuhang Yao, Han Jin, Alay Shah, Shanshan Han, Zijian Hu, Dimitris Stripelis, Yide Ran, Zhaozhuo Xu, Salman Avestimehr, Chaoyang He
| Challenge: | Large language models (LLMs) are widely used in commercial applications . low latency is crucial due to system latency, query concurrency, and computational resources constraints. |
| Approach: | They propose a system that can be resource-efficiently served by addressing bottlenecks beyond LLM inference . they propose 4.3 speed up over vLLM and 1.5 higher throughput . |
| Outcome: | The proposed system outperforms state-of-the-arts with 1.5 higher throughput . it achieves 4.3 speed up with 64 concurrent requests on Mixtral 8x7B . |
MedAdapter: Efficient Test-Time Adaptation of Large Language Models Towards Medical Reasoning (2024.emnlp-main)
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| Challenge: | Large language models (LLMs) have improved generation and reasoning capabilities compared to traditional BERT-sized models due to massive number of parameters and extensive pre-training on vast textual corpora. |
| Approach: | They propose a unified post-hoc adapter for test-time adaptation of large language models . they propose to fine-tune only a small BERT-sized adapter to rank candidate LLMs . |
| Outcome: | The proposed adapter improves performance on four biomedical tasks without requiring computational resources or sharing data with third parties. |
BMRetriever: Tuning Large Language Models as Better Biomedical Text Retrievers (2024.emnlp-main)
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Ran Xu, Wenqi Shi, Yue Yu, Yuchen Zhuang, Yanqiao Zhu, May Dongmei Wang, Joyce Ho, Chao Zhang, Carl Yang
| Challenge: | Developing effective biomedical retrieval models is important for excelling at knowledge-intensive biomedically tasks but still challenging due to the lack of sufficient publicly annotated biomedic data and computational resources. |
| Approach: | They propose a series of dense retrievers for enhancing biomedical retrieval via unsupervised pre-training on large biomedically corpora, followed by instruction fine-tuning on a combination of labeled datasets and synthetic pairs. |
| Outcome: | Experiments on 5 biomedical tasks across 11 datasets confirm the performance of the retrieval model on various biomedically demanding tasks. |
RoseRAG: Robust Retrieval-augmented Generation with Small-scale LLMs via Margin-aware Preference Optimization (2025.findings-acl)
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| Challenge: | Large language models (LLMs) have impressive performance but face high computational costs and latency, limiting their deployment in resource-constrained settings. |
| Approach: | They propose a robust RAG framework for large language models via Margin-aware Preference Optimization to enhance the accuracy and reliability of SLMs. |
| Outcome: | The proposed framework surpasses state-of-the-art benchmarks on three open-domain question answering tasks. |
EHRAgent: Code Empowers Large Language Models for Few-shot Complex Tabular Reasoning on Electronic Health Records (2024.emnlp-main)
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Wenqi Shi, Ran Xu, Yuchen Zhuang, Yue Yu, Jieyu Zhang, Hang Wu, Yuanda Zhu, Joyce Ho, Carl Yang, May Dongmei Wang
| Challenge: | EHRAgent enables clinicians to interact with EHRs using natural language . reliance on rule-based conversion systems often necessitates additional training or effort from data engineers. |
| Approach: | They propose a large language model agent that generates and executes code in natural language to facilitate clinicians in directly interacting with EHRs. |
| Outcome: | The proposed agent outperforms the strongest baseline by up to 29.6% in success rate on three real-world EHR datasets. |
RepoGenesis: Benchmarking End-to-End Microservice Generation from Readme to Repository (2026.acl-long)
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Zhiyuan Peng, Xin Yin, Pu Zhao, Fangkai Yang, Lu Wang, Ran Jia, Xu Chen, Qingwei Lin, Saravan Rajmohan, Dongmei Zhang
| Challenge: | Existing benchmarks focus on isolated function/class-level generation, neglecting complete microservice repository generation. |
| Approach: | They propose a multilingual benchmark for repository-level end-to-end web microservice generation that reflects real-world development workflows. |
| Outcome: | The benchmark compared 106 repositories across 18 domains and 11 frameworks and 1,258 API endpoints and 2,335 test cases. |
Retrieval-augmented GUI Agents with Generative Guidelines (2025.emnlp-main)
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| Challenge: | GUI agents powered by vision-language models struggle with real-world tasks due to their complex nature and limited training data. |
| Approach: | They propose a lightweight vision-language model that leverages web tutorials at inferencetime to synthesize GUI agents. |
| Outcome: | The proposed agent outperforms baseline GUI agents and surpasses other inference baselines by 2.6% to 13.3% across two model sizes. |
Cold-Start Data Selection for Better Few-shot Language Model Fine-tuning: A Prompt-based Uncertainty Propagation Approach (2023.acl-long)
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| Challenge: | Pre-trained language models (PLMs) have achieved competitive performance with limited labeled data for many NLP tasks. |
| Approach: | They propose a prompt-based data selection method for pre-trained language models fine-tuning under cold-start scenarios. |
| Outcome: | The proposed method outperforms the strongest cold-start data selection baselines on six text classification datasets with 128 labels. |
Rethinking the Role of Prompting Strategies in LLM Test-Time Scaling: A Perspective of Probability Theory (2025.acl-long)
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| Challenge: | Recent studies have shown that scaling test-time compute can also effectively improve reasoning. |
| Approach: | They propose a probabilistic method to efficiently predict scaling performance and identify the best prompting strategy under large sampling times. |
| Outcome: | The proposed method significantly improves the scaling performance of majority voting on large language models. |
Can Large Language Models Mine Interpretable Financial Factors More Effectively? A Neural-Symbolic Factor Mining Agent Model (2024.findings-acl)
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| Challenge: | Existing factor mining models are inefficient and inefficient, resulting in a significant challenge to extract interpretable factors. |
| Approach: | They propose a model that integrates the strengths of both neural and symbolic models for factor mining. |
| Outcome: | The proposed model surpasses the SOTA RankIC and RankICIR in predicting S&P 500 returns on real-world stock market data. |
Intent Contrastive Learning Based on Multi-view Augmentation for Sequential Recommendation (2025.coling-main)
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Bo Pei, Yingzheng Zhu, Guangjin Wang, Huajuan Duan, Wenya Wu, Fuyong Xu, Yizhao Zhu, Peiyu Liu, Ran Lu
| Challenge: | Existing work on intent-related models fails to capture long-term dependencies in user behavior and fails to effectively utilize item relevance. |
| Approach: | They propose a sequential recommendation framework that combine temporal variability with position encoding that has extrapolation properties to encode sequences, thereby expanding the model’s view of user behavior. |
| Outcome: | The proposed model improves on three real datasets by 0.8% to 14.7% compared to baselines. |
FOFO: A Benchmark to Evaluate LLMs’ Format-Following Capability (2024.acl-long)
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| Challenge: | Existing benchmarks fail to assess large language models’ format-following proficiency adequately. |
| Approach: | They propose a benchmark to evaluate large language models' ability to follow complex, domain-specific formats. |
| Outcome: | The proposed framework evaluates large language models' ability to follow complex, domain-specific formats across open-source and closed-source models. |
GroundingGPT: Language Enhanced Multi-modal Grounding Model (2024.acl-long)
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Zhaowei Li, Qi Xu, Dong Zhang, Hang Song, YiQing Cai, Qi Qi, Ran Zhou, Junting Pan, Zefeng Li, Vu Tu, Zhida Huang, Tao Wang
| Challenge: | Existing multi-modal large language models focus on capturing global information while neglecting the fine-grained local information in multimodal inputs. |
| Approach: | They propose an end-to-end language enhanced multi-modal grounding model that performs fine-grained grounding tasks for image, video and audio. |
| Outcome: | The proposed model achieves impressive fine-grained understanding of multi-modal inputs while maintaining or improving its global comprehension capabilities. |
A Training-Free Debiasing Framework with Counterfactual Reasoning for Conversational Emotion Detection (2023.emnlp-main)
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| Challenge: | Existing studies in Emotion Recognition in Conversations (ERC) focus on capturing context-sensitive and speaker-sensitive dependencies, ignoring the unintended dataset biases of data. |
| Approach: | They propose a training-free debiasing framework that extracts biases from the model by generating counterfactual utterances and contexts and mitigates them using simple yet empirically robust element-wise subtraction operations. |
| Outcome: | Experiments on three public datasets show that the proposed framework improves generalization ability and fairness across different ERC models. |
Rethinking Data Mixing from the Perspective of Large Language Models (2026.acl-short)
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Yuanjian Xu, Tianze Sun, Changwei Xu, XinLong Zhao, Jianing Hao, Ran Chen, Yang Liu, Ruijie Xu, Stephen Chen, Guang Zhang
| Challenge: | Existing methods to mix data with LLMs have relied on domain definitions derived from intuition. |
| Approach: | They propose a reweighting framework that restructures data scheduling as a graph-constrained optimization problem. |
| Outcome: | The proposed framework achieves competitive performance on GPT-2 models. |
MapGPT: Map-Guided Prompting with Adaptive Path Planning for Vision-and-Language Navigation (2024.acl-long)
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| Challenge: | Embodied agents equipped with GPT as their brains have extraordinary decision-making and generalization abilities across various tasks. |
| Approach: | They propose a map-based agent that introduces an online linguistic-formed map to encourage global exploration. |
| Outcome: | The proposed agent achieves state-of-the-art zero-shot performance on R2R and REVERIE simultaneously. |
Capturing Minds, Not Just Words: Enhancing Role-Playing Language Models with Personality-Indicative Data (2024.findings-emnlp)
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| Challenge: | Existing role-playing models focus on character knowledge and tones, but lack personality-indicative data to capture characters' minds. |
| Approach: | They propose to enhance role-playing agents (RPAs) via personality-indicative data by asking psychological scales to capture broad aspects of personality traits in individuals. |
| Outcome: | The proposed model exhibits advanced role-playing capabilities for both general and personality-related evaluations. |
DocQueryNet: Value Retrieval with Arbitrary Queries for Form-like Documents (2022.coling-1)
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| Challenge: | Existing methods that only address a fixed set of fields are difficult to use for different form types. |
| Approach: | They propose a value retrieval method with arbitrary queries for form-like documents . they propose 'docQueryNet' to predict target value based on understanding of layout and semantics of a form . |
| Outcome: | The proposed method outperforms existing methods on value retrieval . it improves document understanding on large-scale model pre-training by 17% . |
SimRAG: Self-Improving Retrieval-Augmented Generation for Adapting Large Language Models to Specialized Domains (2025.naacl-long)
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Ran Xu, Hui Liu, Sreyashi Nag, Zhenwei Dai, Yaochen Xie, Xianfeng Tang, Chen Luo, Yang Li, Joyce C. Ho, Carl Yang, Qi He
| Challenge: | Retrieval-augmented generation (RAG) enhances the question answering abilities of large language models (LLMs) however, adapting general-purpose RAG systems to specialized fields poses unique challenges due to distribution shifts and limited access to domain-specific data. |
| Approach: | They propose a method that equips large language models with joint capabilities of question answering and question generation for domain adaptation. |
| Outcome: | Experiments on 11 datasets across three different domains verify the efficacy of SimRAG over baselines by 1.2%–8.6%. |
Contra4: Evaluating Contrastive Cross-Modal Reasoning in Audio, Video, Image, and 3D (2025.emnlp-main)
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Artemis Panagopoulou, Le Xue, Honglu Zhou, Silvio Savarese, Ran Xu, Caiming Xiong, Chris Callison-Burch, Mark Yatskar, Juan Carlos Niebles
| Challenge: | a recent study shows that multimodal models can reason across multiple modalities . a limited number of models are able to reason across a variety of inputs . |
| Approach: | They propose a dataset for contrastive cross-modal reasoning across four modalities . they use human annotations and a mixture-of-models round-trip-consistency filter . |
| Outcome: | a new model evaluates models on multiple modalities to determine which one best answers a natural language prompt . the model must select the one that best satisfies the query and then fine-tune it . state-of-the-art models still achieve only 56% accuracy overall and 42% in four-modal settings . |
ALinFiK: Learning to Approximate Linearized Future Influence Kernel for Scalable Third-Parity LLM Data Valuation (2025.naacl-long)
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Yanzhou Pan, Huawei Lin, Yide Ran, Jiamin Chen, Xiaodong Yu, Weijie Zhao, Denghui Zhang, Zhaozhuo Xu
| Challenge: | Large Language Models (LLMs) heavily rely on high-quality training data, making data valuation crucial for optimizing model performance. |
| Approach: | They propose a third-party data valuation approach that assesses the value of individual data samples and proposes a learning strategy to approximate LinFiK. |
| Outcome: | The proposed approach surpasses baselines in effectiveness and efficiency, showing significant scalability advantages as LLM parameters increase. |
Knowledge-Infused Prompting: Assessing and Advancing Clinical Text Data Generation with Large Language Models (2024.findings-acl)
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Ran Xu, Hejie Cui, Yue Yu, Xuan Kan, Wenqi Shi, Yuchen Zhuang, May Dongmei Wang, Wei Jin, Joyce Ho, Carl Yang
| Challenge: | Clinical natural language processing (NLP) is a subfield that requires the extraction, analysis, and interpretation of unstructured clinical text. |
| Approach: | They propose a model which infuses knowledge into clinical text generation with LLMs for clinical NLP tasks. |
| Outcome: | The proposed model improves performance across 8 clinical NLP tasks and 18 datasets by 7.7%-8.7% on average. |
RAM-EHR: Retrieval Augmentation Meets Clinical Predictions on Electronic Health Records (2024.acl-short)
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| Challenge: | Existing deep learning models for EHRs rely on knowledge from a single source and do not capture the semantic information for medical codes. |
| Approach: | They propose a Retrieval AugMentation pipeline to augment clinical prediction on EHRs . they use multiple knowledge sources to convert them into text and use consistency regularization to capture complementary information from patient visits and summarized knowledge. |
| Outcome: | Experiments on two EHR datasets show that RAM-EHR improves clinical prediction tasks. |
OpenRubrics: Towards Scalable Synthetic Rubric Generation for Reward Modeling and LLM Alignment (2026.acl-long)
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| Challenge: | Existing reward models rely on scalar or pairwise judgments that fail to capture multifaceted nature of human preferences. |
| Approach: | They propose a rubric-based reward model that uses a large collection of prompt, rubric pairs to generate a scalar score or preference label for each response. |
| Outcome: | The proposed model surpasses strong size-matched baselines by 8.4% across multiple benchmarks. |
RAG in the Wild: On the (In)effectiveness of LLMs with Mixture-of-Knowledge Retrieval Augmentation (2026.findings-acl)
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| Challenge: | Retrieval-augmented generation (RAG) enhances large language models by integrating external knowledge retrieved at inference time. |
| Approach: | They evaluate RAG systems using MassiveDS, a large-scale datastore with mixture of knowledge. |
| Outcome: | The proposed approach improves performance on knowledge-intensive NLP tasks. |
xLAM: A Family of Large Action Models to Empower AI Agent Systems (2025.naacl-long)
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Jianguo Zhang, Tian Lan, Ming Zhu, Zuxin Liu, Thai Quoc Hoang, Shirley Kokane, Weiran Yao, Juntao Tan, Akshara Prabhakar, Haolin Chen, Zhiwei Liu, Yihao Feng, Tulika Manoj Awalgaonkar, Rithesh R N, Zeyuan Chen, Ran Xu, Juan Carlos Niebles, Shelby Heinecke, Huan Wang, Silvio Savarese, Caiming Xiong
| Challenge: | Autonomous agents powered by large language models (LLMs) have attracted significant research interest, but there are few standards for developing specialized models for agent tasks. |
| Approach: | They propose a series of large action models with dense and mixture-of-expert architectures that unifies, augments, and synthesizes diverse datasets to enhance agent generalizability and performance. |
| Outcome: | The proposed models outperform GPT-4, Claude-3, and many other models in terms of tool use and outperformed GPT-based models on multiple agent ability benchmarks. |
QCRD: Quality-guided Contrastive Rationale Distillation for Large Language Models (2025.emnlp-main)
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| Challenge: | Recent research has focused on smaller, task-specific models enhanced by distilling knowledge from LLMs, but the diversity and quality of negative knowledge remains understudied. |
| Approach: | They propose a quality-guided contrastive rationale distillation framework that aims to enhance reasoning capabilities through contrastive knowledge learning. |
| Outcome: | The proposed method consistently outperforms existing distillation techniques yielding higher-quality rationales. |
Crab: A Novel Configurable Role-Playing LLM with Assessing Benchmark (2025.acl-long)
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Kai He, Yucheng Huang, Wenqing Wang, Delong Ran, Dongming Sheng, Junxuan Huang, Qika Lin, Jiaxing Xu, Wenqiang Liu, Mengling Feng
| Challenge: | Existing RP-LLMs employ only a single role with numerous dialogues, but Crab enables dynamic configuration of desired roles, thereby enhancing related flexibility and adaptability. |
| Approach: | They propose a Configurable Role-Playing LLM with Assessing Benchmark that combines a Role dataset curation, persona-emodying Llm construction, and comprehensive benchmark creation for RP dialogue generation. |
| Outcome: | The proposed model outperforms existing LLMs in performing fine-grained evaluations of RP while keeping dialogue per role minimal. |