Papers by Yanchi Liu
Distantly-Supervised Joint Extraction with Noise-Robust Learning (2024.findings-acl)
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| Challenge: | Existing approaches to identifying entity pairs and relations with a single model are noisy . Existing methods only consider one source of noise or make decisions using external knowledge . |
| Approach: | They propose a framework that aligns entity mentions with corresponding tags for joint extraction . they propose DENRL, which employs a lightweight transformer backbone for joint tagging . |
| Outcome: | The proposed framework outperforms baseline models on two benchmark datasets with better interpretability. |
Uncertainty-Aware Bootstrap Learning for Joint Extraction on Distantly-Supervised Data (2023.acl-short)
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| Challenge: | Existing approaches to extract entity pairs and their relations from labeled data are noisy and expensive. |
| Approach: | They propose a bootstrap learning approach that is motivated by intuition that the higher the uncertainty of an instance, the more likely the model confidence is inconsistent with the ground truths. |
| Outcome: | The proposed method outperforms baselines and related methods on two large datasets. |
Uncertainty-Aware Test-Time Search for Optimization Problem Solving (2026.acl-long)
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Linlin Yu, Xujiang Zhao, Dong Li, Yanchi Liu, Wei Cheng, Zhengzhang Chen, Chen Zhao, Feng Chen, Haifeng Chen
| Challenge: | Language model hallucinations and limited availability of labeled datasets often result in misaligned formulations, code errors and feasibility failures. |
| Approach: | They propose a Monte Carlo Tree Search framework that automates optimization problems from natural language descriptions with efficiency and reliability. |
| Outcome: | The proposed framework achieves state-of-the-art solution accuracy and reduces token usage. |
Unsupervised Concept Representation Learning for Length-Varying Text Similarity (2021.naacl-main)
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| Challenge: | Existing document similarity approaches suffer from the information gap caused by context and vocabulary mismatches when comparing varying-length texts. |
| Approach: | They propose an unsupervised concept representation learning approach to address this issue . they propose a concept-based document matching method to leverage recognition of local phrase features . |
| Outcome: | The proposed method achieves a better F1 score than baseline models on real-world data sets. |
Decoding Time Series with LLMs: A Multi-Agent Framework for Cross-Domain Annotation (2026.findings-eacl)
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Minhua Lin, Zhengzhang Chen, Yanchi Liu, Xujiang Zhao, Zongyu Wu, Junxiang Wang, Xiang Zhang, Suhang Wang, Haifeng Chen
| Challenge: | Time series data is ubiquitous across various domains, including manufacturing, finance, and healthcare. |
| Approach: | They propose a multi-agent system to generate general and domain-specific annotations for time series data. |
| Outcome: | The proposed system outperforms existing methods on synthetic and real-world datasets. |
Representation Interventions Enable Lifelong Knowledge Memory Control in LLMs (2026.acl-long)
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Xuyuan Liu, Shengyu Chen, Xinshuai Dong, Yanchi Liu, Xujiang Zhao, Haoyu Wang, Yujun Yan, Haifeng Chen, Zhengzhang Chen
| Challenge: | Large language models (LLMs) produce outdated or inaccurate content. Updating their knowledge efficiently and accurately without costly retraining is a major challenge. |
| Approach: | They propose a robust and scalable method that treats knowledge control as interventions within the model’s representation space. |
| Outcome: | The proposed method achieves fine-grained control over complex, unstructured knowledge while maintaining general utility with frozen base weights. |
Pruning as a Domain-specific LLM Extractor (2024.findings-naacl)
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Nan Zhang, Yanchi Liu, Xujiang Zhao, Wei Cheng, Runxue Bao, Rui Zhang, Prasenjit Mitra, Haifeng Chen
| Challenge: | Large Language Models (LLMs) have exhibited remarkable proficiency across a wide array of NLP tasks. |
| Approach: | They propose a method for pruning large language models using general or task-specific weights to extract a compressed, task-agnostic LLM. |
| Outcome: | The proposed method extracts a compressed, domain-specific, and task- agnostic LLM by identifying LLM weights that are pivotal for general capabilities, like linguistic capability and multi-task solving, and domain- specific knowledge. |
InfuserKI: Enhancing Large Language Models with Knowledge Graphs via Infuser-Guided Knowledge Integration (2024.findings-emnlp)
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| Challenge: | Large Language Models have exceptional capabilities in open generation, yet they encounter difficulties with tasks that require intensive knowledge. |
| Approach: | They propose a framework that integrates unknown knowledge into LLMs without overlap . they propose integrating domain-specific knowledge graphs into Llms to reduce knowledge forgetting . |
| Outcome: | The proposed framework outperforms state-of-the-art baselines in integrating new knowledge into LLMs. |
Open-ended Commonsense Reasoning with Unrestricted Answer Candidates (2023.findings-emnlp)
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Chen Ling, Xuchao Zhang, Xujiang Zhao, Yanchi Liu, Wei Cheng, Mika Oishi, Takao Osaki, Katsushi Matsuda, Haifeng Chen, Liang Zhao
| Challenge: | Current approaches to commonsense reasoning are limited due to limited answer scope. |
| Approach: | They propose to solve a commonsense question without a pre-defined answer scope . they leverage pre-trained language models to iteratively retrieve reasoning paths on the external knowledge base . |
| Outcome: | The proposed method achieves better performance on two commonsense benchmark datasets. |
DeepSieve: Information Sieving via LLM-as-a-Knowledge-Router (2026.findings-eacl)
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Minghao Guo, Qingcheng Zeng, Xujiang Zhao, Yanchi Liu, Wenchao Yu, Mengnan Du, Haifeng Chen, Wei Cheng
| Challenge: | Existing RAG methods lack fine-grained control over query and source sides, resulting in noisy retrieval and shallow reasoning. |
| Approach: | They propose an agentic RAG framework that integrates information sieving via LLM-as-a-knowledge-router. |
| Outcome: | Experiments on multi-hop QA tasks across heterogeneous sources demonstrate improved reasoning depth, retrieval precision, and interpretability over conventional approaches. |
Uncertainty Quantification for In-Context Learning of Large Language Models (2024.naacl-long)
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Chen Ling, Xujiang Zhao, Xuchao Zhang, Wei Cheng, Yanchi Liu, Yiyou Sun, Mika Oishi, Takao Osaki, Katsushi Matsuda, Jie Ji, Guangji Bai, Liang Zhao, Haifeng Chen
| Challenge: | Existing studies on in-context learning have focused on quantifying the uncertainty associated with the model's response, but they neglect the complexity of the LLM and the uniqueness of in-constitut learning. |
| Approach: | They propose a method to quantify the uncertainty associated with in-context learning and propose corresponding estimation method to quantify both types of uncertainties. |
| Outcome: | The proposed method offers an unsupervised way to understand the prediction of in-context learning in a plug-and-play fashion. |
Multi-Agent Procedural Graph Extraction with Structural and Logical Refinement (2026.findings-eacl)
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Wangyang Ying, Yanchi Liu, Xujiang Zhao, Wei Cheng, Zhengzhang Chen, Wenchao Yu, Yanjie Fu, Haifeng Chen
| Challenge: | Recent advances in large language models (LLMs) show potential for graph extraction, but often yield ill-formed structures or misinterpret logical constructs such as gateways. |
| Approach: | They propose a framework that treats procedural graph extraction as a multi-round reasoning process with structural and logical refinement agents. |
| Outcome: | The proposed framework achieves significant improvements in structural correctness and logical consistency over strong baselines. |
Uncertainty Propagation on LLM Agent (2025.acl-long)
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Qiwei Zhao, Dong Li, Yanchi Liu, Wei Cheng, Yiyou Sun, Mika Oishi, Takao Osaki, Katsushi Matsuda, Huaxiu Yao, Chen Zhao, Haifeng Chen, Xujiang Zhao
| Challenge: | Existing methods for estimating uncertainty in large language models (LLMs) focus on final-step outputs, which fail to account for cumulative uncertainty over multi-step decision-making process and dynamic interactions between agents and their environments. |
| Approach: | They propose a framework that propagates uncertainty through each step of an LLM-based agent’s reasoning process. |
| Outcome: | Extensive experiments on benchmark datasets show that the proposed framework outperforms state-of-the-art methods by 20%. |
Large Language Models Can Be Contextual Privacy Protection Learners (2024.emnlp-main)
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Yijia Xiao, Yiqiao Jin, Yushi Bai, Yue Wu, Xianjun Yang, Xiao Luo, Wenchao Yu, Xujiang Zhao, Yanchi Liu, Quanquan Gu, Haifeng Chen, Wei Wang, Wei Cheng
| Challenge: | Large Language Models (LLMs) have demonstrated remarkable linguistic comprehension and generation capability, but when applied to specialized industries, they face challenges such as hallucination, insufficient domain knowledge, and failing to incorporate the latest domain knowledge. |
| Approach: | They propose a paradigm for fine-tuning LLMs that effectively injects domain-specific knowledge while safeguarding inference-time data privacy. |
| Outcome: | The proposed model protects private data while enhancing the model's knowledge. |
MixLLM: Dynamic Routing in Mixed Large Language Models (2025.naacl-long)
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Xinyuan Wang, Yanchi Liu, Wei Cheng, Xujiang Zhao, Zhengzhang Chen, Wenchao Yu, Yanjie Fu, Haifeng Chen
| Challenge: | Large Language Models (LLMs) exhibit potential artificial generic intelligence, however, their usage is costly with high response latency. |
| Approach: | They develop a dynamic contextual-bandit-based routing system for query-LLM assignment that leverages query tags to enhance query embeddings. |
| Outcome: | The proposed model maximizes response quality and minimizes cost and latency. |