Papers by Ran He
SumSurvey: An Abstractive Dataset of Scientific Survey Papers for Long Document Summarization (2024.findings-acl)
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| Challenge: | a growing need for long document summarization datasets with 16k input is causing problems. |
| Approach: | They propose to use a dataset to analyze salient information in long document summarizations. |
| Outcome: | The proposed dataset outperforms existing models and LLMs in the distribution form of salient information and the distribution of salinal information is an indicator of quality. |
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 . |
HermEs: Interactive Spreadsheet Formula Prediction via Hierarchical Formulet Expansion (2023.acl-long)
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Wanrong He, Haoyu Dong, Yihuai Gao, Zhichao Fan, Xingzhuo Guo, Zhitao Hou, Xiao Lv, Ran Jia, Shi Han, Dongmei Zhang
| Challenge: | HermEs is a spreadsheet formula prediction language that is difficult for Excel users without programming experience to master. |
| Approach: | They propose a hierarchical approach to formula prediction via HiEraRchical forMulet ExpanSion . they propose generating formulas in a fixed order using hierarchically generated formulas . |
| Outcome: | The proposed approach improves formula prediction accuracy by guaranteeing correct grammar and streamlining token-level decoding with high-level Formulet. |
Benchmarking Large Language Models Under Data Contamination: A Survey from Static to Dynamic Evaluation (2025.emnlp-main)
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Simin Chen, Yiming Chen, Zexin Li, Yifan Jiang, Zhongwei Wan, Yixin He, Dezhi Ran, Tianle Gu, Haizhou Li, Tao Xie, Baishakhi Ray
| Challenge: | In the era of evaluating large language models, data contamination is an increasingly prominent concern . static benchmarking has been used for evaluation, but there are limitations of *dynamic* benchmarks . |
| Approach: | They propose a series of optimal design principles for *dynamic* benchmarking and analyze the limitations of existing *static* benchmarks. |
| Outcome: | The proposed benchmarks highlight a critical gap in the evaluation of LLMs. |
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. |
MELM: Data Augmentation with Masked Entity Language Modeling for Low-Resource NER (2022.acl-long)
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| Challenge: | Named entity recognition (NER) tasks have limited amount of labeled data . data augmentation methods suffer from token-label misalignment, which leads to unsatsifactory performance. |
| Approach: | They propose a data augmentation framework that explicitly injects NER labels into sentence context and generates high-quality augmented data with novel entities. |
| Outcome: | The proposed framework outperforms baseline methods on low-resource tasks. |
What If Consensus Lies? Selective-Complementary Reinforcement Learning at Test Time (2026.acl-long)
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| Challenge: | Existing TTRL methods rely on positive pseudo-labeling strategies to enhance reasoning capabilities. |
| Approach: | They propose a test-time reinforcement learning framework that mitigates label noise amplification by deriving pseudo-rewards from majority voting consensus. |
| Outcome: | The proposed framework mitigates label noise amplification by implementing selective positive pseudo-labeling and entropy-gated negative p-labeled pruning. |
InfiMM: Advancing Multimodal Understanding with an Open-Sourced Visual Language Model (2024.findings-acl)
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Haogeng Liu, Quanzeng You, Yiqi Wang, Xiaotian Han, Bohan Zhai, Yongfei Liu, Wentao Chen, Yiren Jian, Yunzhe Tao, Jianbo Yuan, Ran He, Hongxia Yang
| Challenge: | InfiMM is a multimodal large language model that adapts to complex vision-language tasks. |
| Approach: | They present a Multimodal Large Language Model that adapts to intricate vision-language tasks using large-scale training data and comprehensive training strategies. |
| Outcome: | Empirical evaluations across a variety of benchmarks underscore InfiMM’s remarkable capability in multimodal understanding. |
Decoupling Mixture-of-Graphs: Unseen Relational Learning for Knowledge Graph Completion by Fusing Ontology and Textual Experts (2022.coling-1)
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| Challenge: | Existing methods for knowledge Graph Completion (KGC) fail in unseen relation representations. |
| Approach: | They propose to use three kinds of graphs to obtain unseen relation representations . they propose to decouple mixture-of-graph experts (DMoG) for unseened relations learning . |
| Outcome: | The proposed method outperforms the state-of-the-art methods on unseen relation representations. |
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%. |
Does Large Language Model Contain Task-Specific Neurons? (2024.emnlp-main)
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| Challenge: | Large language models (LLMs) have demonstrated remarkable capabilities in comprehensively handling various types of natural language processing (NLP) tasks. |
| Approach: | They propose a method for task-specific neuron localization based on Causal Gradient Variation with Special Tokens (CGVST) this method identifies task- specific neurons by concentrating on the most significant tokens during task processing, eliminating redundant tokens and minimizing interference from non-essential neurons. |
| Outcome: | The proposed method can locate task-specific neurons across eight public tasks. |
InfiMM-WebMath-40B: Advancing Multimodal Pre-Training for Enhanced Mathematical Reasoning (2025.findings-emnlp)
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Xiaotian Han, Yiren Jian, Xuefeng Hu, Haogeng Liu, Yiqi Wang, Qihang Fan, Yuang Ai, Huaibo Huang, Ran He, Zhenheng Yang, Quanzeng You
| Challenge: | InfiMM-WebMath-40B is a dataset of interleaved image-text documents . it consists of 24 million web pages, 85 million image URLs, and 40 billion text tokens . |
| Approach: | InfiMM-WebMath-40B is a high-quality dataset of interleaved image-text documents . it contains 24 million web pages, 85 million image URLs, and 40 billion text tokens . |
| Outcome: | InfiMM-WebMath-40B is a high-quality dataset of interleaved image-text documents . it consists of 24 million web pages, 85 million image URLs, and 40 billion text tokens . |
Understanding and Mitigating Spurious Signal Amplification in Test-Time Reinforcement Learning for Math Reasoning (2026.findings-acl)
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| Challenge: | a framework to mitigate spurious optimization signals is proposed for test-time reinforcement learning (TTRL) Reinforcement learning with verifiable rewards (RLVR) is an effective paradigm for improving large language models on structured challenging reasoning tasks. |
| Approach: | They propose a framework to mitigate spurious optimization signals from label noise . they propose to use a frequency-based sampling strategy to exclude ambiguous samples . |
| Outcome: | The proposed framework outperforms existing TTRL baselines on three large language models across multiple mathematical reasoning benchmarks. |
Multilingual Knowledge Graph Completion from Pretrained Language Models with Knowledge Constraints (2023.findings-acl)
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| Challenge: | Existing methods for multilingual knowledge graph completion do not align with mKGC tasks because of their English-centric bias. |
| Approach: | They propose to use multilingual pretrained language models to solve queries in different languages by reasoning a tail entity. |
| Outcome: | The proposed method outperforms the previous SOTA on Hits@1 and Hits @10 by 12.32% and 16.03% on public datasets. |
Multilingual Knowledge Graph Completion via Efficient Multilingual Knowledge Sharing (2025.findings-emnlp)
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| Challenge: | Existing MKGC research ignores the shareability of cross-lingual knowledge. |
| Approach: | They propose a multilingual knowledge Graph Completion framework that leverages multilingual shared knowledge to significantly enhance performance through two components: Knowledge-level Grouped Mixture of Experts (KL-GMoE) and Iterative Entity Reranking (IER). |
| Outcome: | The proposed framework achieves improvements of 5.47%, 3.27%, and 1.01% in the Hits@1, Hits @3, and Hits_10 metrics, respectively, compared with existing state-of-the-art (SOTA) MKGC method. |
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. |
Wav2SQL: Direct Generalizable Speech-To-SQL Parsing (2024.findings-acl)
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| Challenge: | Existing models for speech-driven SQL parsing are based on a cascaded approach, resulting in data scarcity and inconsistent performance. |
| Approach: | They propose a direct generalizable speech-to-SQL parsing model which avoids error compounding across cascaded systems. |
| Outcome: | The proposed model avoids error compounding and achieves state-of-the-art results by 4.7% improvement over baseline. |