Papers by Yongfeng Chen
Personalized Transformer for Explainable Recommendation (2021.acl-long)
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| Challenge: | Recent years have witnessed the successful application of natural language generation. |
| Approach: | They propose a model that uses user and item IDs to predict the words in the target explanation to make personalized Transformer. |
| Outcome: | The proposed model outperforms BERT on the explainable recommendation task in terms of effectiveness and efficiency. |
Large Language Models for Generative Recommendation: A Survey and Visionary Discussions (2024.lrec-main)
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| Challenge: | Large language models (LLMs) have revolutionized the field of natural language processing but are not fully able to leverage the generative power of LLM. |
| Approach: | They examine the progress, methods, and future directions of large language models . they examine what generative recommendation is, why RS should advance to generative recommendations . |
| Outcome: | The proposed approach can be simplified to generate recommendations from the entire pool of items. |
SEMA-RAG: A Self-Evolving Multi-Agent Retrieval-Augmented Generation Framework for Medical Reasoning (2026.findings-acl)
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| Challenge: | Standard RAG frameworks treat retrieval as a static, single-round auxiliary step . compressed workflow makes it difficult to form reliable evidence chains . |
| Approach: | They propose a framework that decouples tasks and allows for dynamic multi-round exploration . they propose retrieval-augmented generation (RAG) to mitigate hallucinations and knowledge obsolescence . |
| Outcome: | The proposed framework improves the strongest baseline by *+6.46* accuracy points on average across five benchmarks and five LLM backbones. |
KnowVrDU: A Unified Knowledge-aware Prompt-Tuning Framework for Visually-rich Document Understanding (2024.lrec-main)
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Yunqi Zhang, Yubo Chen, Jingzhe Zhu, Jinyu Xu, Shuai Yang, Zhaoliang Wu, Liang Huang, Yongfeng Huang, Shuai Chen
| Challenge: | Existing methods for integrating layout and image features into pre-training language models are not suitable for few-shot settings. |
| Approach: | They propose to reformulate VrDU tasks into a single question-answering format with task-specific prompts and train the pre-trained model with the parameter-efficient prompt tuning method. |
| Outcome: | The proposed framework can be used in few-shot settings and reduces data requirements. |
Learning Reasoning Patterns for Relational Triple Extraction with Mutual Generation of Text and Graph (2022.findings-acl)
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| Challenge: | Existing methods focused on learning text patterns from explicit mentions but failed to extract the implicitly implied triples. |
| Approach: | They propose to construct a relational graph from a sentence and apply multi-layer graph convolutions to capture the type inference logic of the paths. |
| Outcome: | The proposed framework can find multi-hop reasoning paths and capture type inference logic with the sentence's supplementary relational expressions. |
Thought-Action Graph Reasoning: Faithful and Efficient Reasoning of Large Language Models via Reusing Past Experience (2026.findings-acl)
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Zhixiao Qi, Feng Huang, Yunqi Zhang, Shijie Zhang, Qingqing Sun, Yongfeng Huang, Minghu Jiang, Shuai Chen, Tianyi Zhang
| Challenge: | Existing methods for integrating knowledge graphs with LLMs suffer from poor generalization or low reasoning efficiency. |
| Approach: | They propose a thought-action Graph (TAG) that decomposes LLM-KG interaction trajectories into fine-grained semantic operators and guides LLM to execute on them. |
| Outcome: | The proposed paradigm outperforms state-of-the-art methods on KGQA benchmarks while reducing the number of LLM calls and generated tokens. |
Retrieval-Augmented Generation with Hierarchical Knowledge (2025.findings-emnlp)
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Haoyu Huang, Yongfeng Huang, Yang Junjie, Zhenyu Pan, Yongqiang Chen, Kaili Ma, Hongzhi Chen, James Cheng
| Challenge: | Existing RAG methods do not utilize hierarchical knowledge in human cognition, which limits the capabilities of RAG systems. |
| Approach: | They propose a graph-based approach that utilizes hierarchical knowledge to enhance the semantic understanding and structure capturing capabilities of RAG systems. |
| Outcome: | The proposed approach achieves significant performance improvements over the state-of-the-art methods. |
Long-context Language Models Fail in Basic Retrieval Tasks Without Sufficient Reasoning Steps (2025.findings-emnlp)
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| Challenge: | despite their extensive context window, long-context language models fail in some basic cases . a recent study shows that long-cot methods are not necessary for long-constituency tasks . |
| Approach: | a new study evaluates long-context language models with a large context window . the authors propose a method that can be well addressed with arbitrary reasoning steps . |
| Outcome: | The proposed methods are well addressed with a sufficient number of reasoning steps, guided by specific CoT prompts. |
Jointly Extracting Explicit and Implicit Relational Triples with Reasoning Pattern Enhanced Binary Pointer Network (2021.naacl-main)
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| Challenge: | Existing methods for relational triple extraction ignore implicit triples that lack explicit expressions, leading to incomplete knowledge graphs. |
| Approach: | They propose a binary pointer network to extract explicit and implicit relational triples from sentences and to retain the information of extracted triples in an external memory. |
| Outcome: | The proposed framework extracts overlapping triples relevant to each word sequentially and retains the information of extracted triples in an external memory. |
MemRec: Collaborative Memory-Augmented Agentic Recommender System (2026.acl-long)
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Weixin Chen, Yuhan Zhao, Jingyuan Huang, Zihe Ye, Mingxuan Ju, Tong Zhao, Neil Shah, Li Chen, Yongfeng Zhang
| Challenge: | Existing recommender systems rely on semantic user and item memories to make predictions, but these memories are kept in isolation. |
| Approach: | They propose a framework that architecturally decouples memory management from reasoning to decouple memory management and reasoning from the user and item memories. |
| Outcome: | The proposed framework decouples memory management from reasoning and achieves state-of-the-art performance on four benchmarks. |
RelU-Net: Syntax-aware Graph U-Net for Relational Triple Extraction (2022.emnlp-main)
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| Challenge: | Existing methods focused on capturing semantic information but failed to incorporate syntactic structures of the sentence, which is proved to contain rich relational information. |
| Approach: | They propose a framework to capture syntactic information for relational triple extraction by contracting dependency tree into a core relational topology and eliminating redundant information with graph pooling operations. |
| Outcome: | The proposed framework incorporates syntactic information for relational triple extraction. |
H-Mem: Hybrid Multi-Dimensional Memory Management for Long-Context Conversational Agents (2026.eacl-long)
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| Challenge: | Existing frameworks for long-context conversational agents struggle to organize information across dimensions like time and topic, leading to poor retrieval. |
| Approach: | They propose a Hybrid Multi-Dimensional Memory architecture that stores conversational facts in two parallel hierarchical data structures: a temporal tree that organizes information chronologically and a semantic tree that arranges it conceptually. |
| Outcome: | The proposed architecture improves performance on long-context QA datasets by 8.4% compared to current systems. |
Named Entity Recognition in Multi-level Contexts (2020.aacl-main)
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| Challenge: | Existing methods for named entity recognition are unsatisfactory for recognizing entities in limited or ambiguous sentence-level contexts. |
| Approach: | They propose a framework to incorporate multi-level contexts for named entity recognition using TagLM as a baseline model and an auxiliary task to mine word-level contextual information. |
| Outcome: | The proposed framework is based on a set of sentence-level contexts and a document-level task to mine word-level contextual information. |
ERNIE-Layout: Layout Knowledge Enhanced Pre-training for Visually-rich Document Understanding (2022.findings-emnlp)
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Qiming Peng, Yinxu Pan, Wenjin Wang, Bin Luo, Zhenyu Zhang, Zhengjie Huang, Yuhui Cao, Weichong Yin, Yongfeng Chen, Yin Zhang, Shikun Feng, Yu Sun, Hao Tian, Hua Wu, Haifeng Wang
| Challenge: | Existing methods for visually rich document understanding lack layout-centered knowledge . experimental results show that ERNIE-Layout improves layout awareness . |
| Approach: | They propose a document pre-training solution with layout knowledge enhancement in the whole workflow to learn better representations that combine the features from text, layout, and image. |
| Outcome: | The proposed model outperforms existing models on key downstream tasks. |
CLEVA: Chinese Language Models EVAluation Platform (2023.emnlp-demo)
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Yanyang Li, Jianqiao Zhao, Duo Zheng, Zi-Yuan Hu, Zhi Chen, Xiaohui Su, Yongfeng Huang, Shijia Huang, Dahua Lin, Michael Lyu, Liwei Wang
| Challenge: | Large language models (LLMs) have revolutionized natural language processing. |
| Approach: | They propose a Chinese-based platform that assesses Chinese LLMs using a standardized workflow and a unique sampling strategy. |
| Outcome: | CLEVA evaluates Chinese LLMs on a standardized workflow and a competitive leaderboard with minimal coding. |