Papers by Yongqi Chen
Aligning VLM Assistants with Personalized Situated Cognition (2025.acl-long)
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Yongqi Li, Shen Zhou, Xiaohu Li, Xin Miao, Jintao Wen, Mayi Xu, Jianhao Chen, Birong Pan, Hankun Kang, Yuanyuan Zhu, Ming Zhong, Tieyun Qian
| Challenge: | Existing studies on vision-language models aligned with general human objectives have not been successful because people with diversified backgrounds have different cognition even in the same situation. |
| Approach: | They propose to characterize individuals based on the sociological concept of Role-Set and then evaluate their actions to see whether personalized alignment is achieved. |
| Outcome: | The proposed framework constructs a cognition-aware and action-based reward model for personalized alignment. |
Track-SQL: Enhancing Generative Language Models with Dual-Extractive Modules for Schema and Context Tracking in Multi-turn Text-to-SQL (2025.naacl-long)
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| Challenge: | Existing approaches to generative language models struggle to handle the increasing complexity of multi-turn Text-to-SQL tasks. |
| Approach: | They propose a framework which enhances generative language models with dual-extractive modules designed to track schema and contextual changes in multi-turn Text-to-SQL. |
| Outcome: | The proposed framework achieves state-of-the-art performance on SparC and CoSQL datasets and significantly improves execution accuracy in multi-turn interactions by 7.1% and 9.55%. |
Distributed LLM Serving on Consumer-Grade GPUs by Reconciling Computation and Communication (2025.findings-emnlp)
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Lewei Jin, Kui Zhang, Yongqi Chen, null Zhuoyifan, Renjie Li, Yi Gao, Bowei Yang, Zhengong Cai, Wei Dong
| Challenge: | Large language models are reshaping internet services, and serving them is costly. |
| Approach: | They propose an efficient distributed LLM serving system that splits prefill and decode requests into smaller chunks . |
| Outcome: | The proposed system reduces TTFT, TPOT, and latency compared to the state-of-the-art system. |
Travel on the ICD Tree: Benchmarking Agentic Reasoning for ICD Coding from Chinese Electronic Medical Records (2026.findings-acl)
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| Challenge: | Accurate International Classification of Diseases (ICD) coding is crucial for hospital management and healthcare data governance. |
| Approach: | They propose a framework to evaluate ICD coding based on complete EMRs . they use a dataset of 560 real clinical records covering 434 common diseases . |
| Outcome: | The proposed framework explores the capability boundaries of large language models under different paradigms. |
CACA: Context-Aware Cross-Attention Network for Extractive Aspect Sentiment Quad Prediction (2025.coling-main)
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| Challenge: | Existing generative ASQP approaches do not model the contextual relationship of the review sentence to predict implicit terms. |
| Approach: | They propose an extractive ASQP framework, CACA, which features with Context-Aware Cross-Attention Network to enhance alignment of aspects and opinions. |
| Outcome: | The proposed framework improves the alignment of aspects and opinions, whether explicit or implicit, and improves on three benchmark datasets. |
ArchiDocGen: Multi-Agent Framework for Expository Document Generation in the Architectural Industry (2025.acl-industry)
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Junjie Jiang, Haodong Wu, Yongqi Zhang, Songyue Guo, Bingcen Liu, Caleb Chen Cao, Ruizhe Shao, Chao Guan, Peng Xu, Lei Chen
| Challenge: | drafting method statements is labor-intensive and time-consuming . traditional methods involve using static templates filled in manually by engineers . |
| Approach: | They propose a framework that automates method statement generation by using multi-agent collaboration. |
| Outcome: | The proposed framework achieves 4.38 ContentScore, excelling in specialization, completeness, organization, and clarity. |
Learning to Describe for Predicting Zero-shot Drug-Drug Interactions (2023.emnlp-main)
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| Challenge: | Existing computational methods for DDI prediction fail to capture interactions for new drugs due to the lack of knowledge. |
| Approach: | They propose a problem setup as zero-shot DDI prediction that deals with the case of new drugs by using textual information from online databases. |
| Outcome: | The proposed method improves on several settings including zero-shot and few-shot DDI prediction and the selected texts are semantically relevant. |
R2AG: Incorporating Retrieval Information into Retrieval Augmented Generation (2024.findings-emnlp)
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| Challenge: | Existing approaches to augment large language models with external documents are lacking in the semantic gap between LLMs and retrievers due to differences in their training objectives and architectures. |
| Approach: | They propose to integrate R2AG into R2etrieval augmented generation framework by using a R2-Former to capture retrieval information. |
| Outcome: | The proposed framework fills the semantic gap between LLMs and retrievers due to differences in their training objectives and architectures. |
Praetor: A Fine-Grained Generative LLM Evaluator with Instance-Level Customizable Evaluation Criteria (2025.acl-long)
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Yongqi Leng, Renren Jin, Yue Chen, Zhuowen Han, Ling Shi, Jianxiang Peng, Lei Yang, Juesi Xiao, Deyi Xiong
| Challenge: | Existing evaluation methods are inadequate to evaluate large language models (LLMs). |
| Approach: | They propose a fine-grained generative LLM evaluator with instance-level customazable evaluation criteria that can be used to evaluate large language models. |
| Outcome: | The proposed model outperforms existing LLM evaluators and instruction-tuned LLMs on multiple benchmarks and sets new SOTA results. |
BubbleRAG: Interactive Cognitive Offloading with Thought Bubble in Retrieval-Augmented Generation (2026.findings-acl)
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| Challenge: | Retrieval-augmented generation (RAG) extends the capabilities of large language models (LLMs) by providing access to external knowledge. |
| Approach: | They propose a framework that emulates human interactive reading through annotation and re-reading by integrating a thought bubble module that offloads internal cognition into external bookmark tokens, which are then annotated back into the context. |
| Outcome: | The proposed framework offloads internal cognition into external bookmark tokens, which are then annotated back into the context. |
BPO: Towards Balanced Preference Optimization between Knowledge Breadth and Depth in Alignment (2025.naacl-long)
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| Challenge: | Reinforcement Learning with Human Feedback (RLHF) is the key to the success of large language models (LLMs) in recent years. |
| Approach: | They propose a method to balance the number of prompts and responses to improve knowledge breadth and knowledge depth by introducing gradient-based clustering to estimate the knowledge informativeness and usefulness of each augmented sample. |
| Outcome: | The proposed method outperforms baseline methods while maintaining training efficiency. |
MultiConIR: Towards Multi-Condition Information Retrieval (2025.findings-emnlp)
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Xuan Lu, Sifan Liu, Bochao Yin, Yongqi Li, Xinghao Chen, Hui Su, Yaohui Jin, Wenjun Zeng, Xiaoyu Shen
| Challenge: | MultiConIR is a benchmark designed to evaluate retrieval and reranking models under nuanced multi-condition query scenarios. |
| Approach: | They propose a benchmark to evaluate retrieval and reranking models under nuanced multi-condition query scenarios. |
| Outcome: | The proposed benchmark evaluates retrieval and reranking models under nuanced multi-condition query scenarios across five domains. |
Reasoning in the Dark: Interleaved Vision-Text Reasoning in Latent Space (2026.findings-acl)
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| Challenge: | Existing multimodal reasoning methods depend on explicit reasoning steps that require labor-intensive vision-text annotations and inherently introduce significant inference latency. |
| Approach: | They propose a method that integrates visual and visual information into the reasoning process to improve the performance of multimodal LLMs. |
| Outcome: | The proposed method achieves an average performance increase of 5.45% while achieving a speed increase of over 5 times compared to existing methods. |
S2GSL: Incorporating Segment to Syntactic Enhanced Graph Structure Learning for Aspect-based Sentiment Analysis (2024.acl-long)
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| Challenge: | Existing graph-based approaches to learn static structures and dynamic latent trees are lacking in incorporating semantic and syntactic information simultaneously within complex global structures. |
| Approach: | They propose a graph-based framework that incorporates semantic and syntactic information simultaneously within global structures. |
| Outcome: | The proposed framework removes irrelevant contexts and syntactic dependencies and achieves complementarity across diverse structures. |
Perovskite-LLM: Knowledge-Enhanced Large Language Models for Perovskite Solar Cell Research (2025.findings-emnlp)
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Xiang Liu, Penglei Sun, Shuyan Chen, Longhan Zhang, Peijie Dong, Huajie You, Yongqi Zhang, Chang Yan, Xiaowen Chu, Tong-yi Zhang
| Challenge: | a rapid advancement of perovskite solar cells has led to an exponential growth in research publications. |
| Approach: | They propose a knowledge-enhanced system for perovskite solar cells that integrates three key components. |
| Outcome: | The proposed system outperforms existing models in domain-specific knowledge retrieval and scientific reasoning tasks. |
TEMA: Anchor the Image, Follow the Text for Multi-Modification Composed Image Retrieval (2026.acl-long)
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| Challenge: | Composed Image Retrieval (CIR) is an image retrieval paradigm that enables users to retrieve a target image using a multimodal query that consists of a reference image and modification text. |
| Approach: | They propose a text-oriented entity mapping architecture that allows users to use a reference image and modification text to retrieve a target image. |
| Outcome: | The proposed framework is superior in both original and multi-modification scenarios while maintaining an optimal balance between retrieval accuracy and computational efficiency. |
SecureSQL: Evaluating Data Leakage of Large Language Models as Natural Language Interfaces to Databases (2024.findings-emnlp)
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| Challenge: | Existing studies on the vulnerability of large language models to SQL injection have been limited. |
| Approach: | They propose to evaluate the potential of language models to leak sensitive data when generating SQL queries. |
| Outcome: | The proposed model with the best performance has an accuracy of 61.7%, compared to humans who achieve 94% accuracy. |