Papers by Ruoyu Liu
Enabling Self-Improving Agents to Learn at Test Time With Human-In-The-Loop Guidance (2025.emnlp-industry)
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Yufei He, Ruoyu Li, Alex Chen, Yue Liu, Yulin Chen, Yuan Sui, Cheng Chen, Yi Zhu, Luca Luo, Frank Yang, Bryan Hooi
| Challenge: | Existing large language model (LLM) agents are unable to adapt to changing domain knowledge and rules. |
| Approach: | They propose an LLM agent framework that continuously learns updated domain knowledge at test time. |
| Outcome: | The proposed agent improves on a customer due diligence name screening task on . the agent learns updated domain knowledge at test time. |
TED-EL: A Corpus for Speech Entity Linking (2024.lrec-main)
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| Challenge: | Current entity linking tasks rely on textual information, but entities usually exist in textual, audio, and visual contexts in real-world data such as social media and video websites. |
| Approach: | They propose a speech entity linking task to recognize mentions from speech and link them to entities in knowledge bases. |
| Outcome: | The proposed model outperforms the existing models on the TED-EL dataset, scoring an F1 score of 60.68%. |
A Middle Path for On-Premises LLM Deployment: Preserving Privacy Without Sacrificing Model Confidentiality (2025.emnlp-main)
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| Challenge: | Privacy-sensitive users require deploying large language models within their own infrastructure (on-premises) vulnerabilities in local environments can lead to unauthorized access and potential model theft. |
| Approach: | They propose a framework that secures a few bottom layers in a secure environment . they propose metric to optimize trade-off between protection and customization flexibility . |
| Outcome: | The proposed framework outperforms baselines on five models with 1.3B to 70B parameters. |
Improving Grammatical Error Correction via Pre-Training a Copy-Augmented Architecture with Unlabeled Data (N19-1)
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| Challenge: | Neural machine translation systems have become state-of-the-art approaches for Grammatical Error Correction (GEC) task. |
| Approach: | They propose a copy-augmented architecture for the Grammatical Error Correction task by copying unchanged words from the source sentence to the target sentence. |
| Outcome: | The proposed architecture outperforms all recently published state-of-the-art results by a large margin. |
MultiFinBen: Benchmarking Large Language Models for Multilingual and Multimodal Financial Application (2026.acl-long)
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Xueqing Peng, Lingfei Qian, Yan Wang, Ruoyu Xiang, Yueru He, Yang Ren, Mingyang Jiang, Vincent Jim Zhang, Yuqing Guo, Jeff Zhao, Huan He, Yi Han, Yun Feng, Yuechen Jiang, Yupeng Cao, Haohang Li, Yangyang Yu, Xiaoyu Wang, Penglei Gao, Shengyuan Lin, Keyi Wang, Shanshan Yang, Yilun Zhao, Zhiwei Liu, Peng Lu, Jerry Huang, Suyuchen Wang, Triantafillos Papadopoulos, Polydoros Giannouris, Efstathia Soufleri, Nuo Chen, Zhiyang Deng, Heming Fu, Yijia Zhao, Mingquan Lin, Meikang Qiu, Kaleb E Smith, Arman Cohan, Xiao-Yang Liu, Jimin Huang, Guojun Xiong, Alejandro Lopez-Lira, Xi Chen, Junichi Tsujii, Jian-Yun Nie, Sophia Ananiadou, Qianqian Xie
| Challenge: | Existing evaluations of LLMs in finance are text-only, monolingual, and largely saturated by current models. |
| Approach: | They propose a multilingual and multimodal benchmark for evaluating LLMs in real financial contexts. |
| Outcome: | The first expert-annotated multilingual and multimodal benchmark is released . it evaluates 21 leading LLMs and shows they perform better in multilingual settings . |
Not All Citations Are Equal:Entropy-Guided Citation Selection for Noise-Resistant Medical LLM (2026.findings-acl)
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| Challenge: | Large language models have demonstrated extensive potential in medical applications . however, their practical deployment in healthcare faces significant challenges . |
| Approach: | They propose a training-free multi-turn reasoning framework and a post-training methodology that provides external knowledge support for large language models. |
| Outcome: | The proposed framework elicits internal thought, external thought, and fusion thought, with an entropy-based reward that encourages selective citation of beneficial external knowledge while penalizing noisy citations. |
AceGPT, Localizing Large Language Models in Arabic (2024.naacl-long)
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Huang Huang, Fei Yu, Jianqing Zhu, Xuening Sun, Hao Cheng, Song Dingjie, Zhihong Chen, Mosen Alharthi, Bang An, Juncai He, Ziche Liu, Junying Chen, Jianquan Li, Benyou Wang, Lian Zhang, Ruoyu Sun, Xiang Wan, Haizhou Li, Jinchao Xu
| Challenge: | Significant concerns emerge when addressing cultural sensitivity and local values. |
| Approach: | They propose a localized Large Language Model (LLM) specifically for Arabic, a language imbued with unique cultural characteristics inadequately addressed by current mainstream models. |
| Outcome: | The proposed model sets the state-of-the-art standard for open Arabic LLMs across various benchmarks. |
Rethinking Data Mixture for Large Language Models: A Comprehensive Survey and New Perspectives (2026.findings-eacl)
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| Challenge: | Existing methods for data mixture improve the generalization capability of large language models (LLMs) on downstream tasks. |
| Approach: | They propose a fine-grained categorization of existing methods and propose three subtypes of offline and online methods. |
| Outcome: | The proposed methods extend beyond offline and online classifications and highlight key challenges in the field of data mixture. |
LGA: LLM-GNN Aggregation for Temporal Evolution Attribute Graph Prediction (2025.emnlp-main)
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| Challenge: | Current methods focus on 1-hop neighborhood aggregation, lacking capability to capture complex structural interactions. |
| Approach: | They propose a framework that integrates structural information into attribute embeddings through an attribute embedded loss. |
| Outcome: | The proposed framework shows significant improvements over existing methods on real-world datasets. |
Towards A Better Initial Policy Model For Scalable Long-CoT Reinforcement Learning (2025.findings-acl)
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| Challenge: | Long-CoT reasoning and reinforcement learning are demonstrating remarkable performance and scalability, however, there is a lack of systematic guidelines for obtaining a better initial policy model. |
| Approach: | They propose a systematic guideline and a novel Re-RFT method to obtain more efficient reasoning patterns from different initial models. |
| Outcome: | The proposed method surpasses DeepSeek-R1-Distill-Qwen-14B model by 4.6%, demonstrating its effectiveness and superiority. |
Denoising based Sequence-to-Sequence Pre-training for Text Generation (D19-1)
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| Challenge: | PoDA pre-trains encoders and decoders by denoising noise-corrupted text . Unlike encoder-only or decode-only methods, it can be used for text generation tasks without using any task-specific techniques. |
| Approach: | They propose a sequence-to-sequence (seq2sequ) pre-training method PoDA which denoises autoencoders by denoising noise-corrupted text. |
| Outcome: | The proposed method improves model performance over strong baselines without using any task-specific techniques and significantly speed up convergence. |
Interactive Evaluation for Medical LLMs via Task-oriented Dialogue System (2025.coling-main)
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| Challenge: | In typical medical scenarios, doctors often ask a set of questions to gain a comprehensive understanding of patients’ conditions. |
| Approach: | They propose to use multi-turn medical dialogue evaluation to evaluate proactive communication and diagnostic capabilities of medical Large Language Models (LLMs) . |
| Outcome: | The proposed model outperforms existing models on multi-turn question-answering datasets and is therefore cost-effective. |
Multi-Perspective Context Aggregation for Semi-supervised Cloze-style Reading Comprehension (C18-1)
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| Challenge: | Recent studies have shown that cloze-style reading comprehension is a popular task for measuring the progress of natural language understanding. |
| Approach: | They propose a multi-perspective framework which can be seen as joint training of heterogeneous experts and aggregate context information from different perspectives. |
| Outcome: | The proposed framework achieves new state-of-the-art over previous strong baselines on a recently released cloze-test dataset. |