Papers by Yan Ling
FLIQA-AD: a Fusion Model with Large Language Model for Better Diagnose and MMSE Prediction of Alzheimer’s Disease (2025.naacl-short)
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| Challenge: | Existing classification and regression models that only extract finer-grained information from magnetic resonance imaging (MRI) may not be effective for Alzheimer's disease (AD). |
| Approach: | They propose to use a 3D Adapter in a Vision Transformer to extract the patient's EHR information and questions related to the disease as text prompts. |
| Outcome: | The proposed model can discriminate and predict the corresponding MMSE score based on the extracted brain structural information and textual content . |
Retrieving, Rethinking and Revising: The Chain-of-Verification Can Improve Retrieval Augmented Generation (2024.findings-emnlp)
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| Challenge: | Recent Retrieval Augmented Generation (RAG) aims to enhance Large Language Models . however, such approach can generate inconsistent answer with external references . |
| Approach: | They propose to integrate the verification module into the RAG to improve external retrieval correctness and internal generation consistency. |
| Outcome: | The proposed model can significantly surpass the state-of-the-art baselines using different LLM backbones. |
RPO: Retrieval Preference Optimization for Robust Retrieval-Augmented Generation (2025.acl-long)
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| Challenge: | Large language models struggle to evaluate the correctness of non-parametric knowledge when it differs from internal memorization, leading to knowledge conflicts during response generation. |
| Approach: | They propose a lightweight alignment method to leverage multi-source knowledge based on retrieval relevance. |
| Outcome: | Experiments on four datasets show that the proposed method outperforms RAG by 4-10% in accuracy without any extra component. |
Think-Search-Patch: A Retrieval-Augmented Reasoning Framework for Repository-Level Code Repair (2025.emnlp-industry)
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Bojian Xiong, Yikun Lei, Xikai Liu, Shaowei Zhang, Pengyun Zhu, Yan Liu, Yongqi Leng, Ling Shi, Meizhi Zhong, Yurong Zhang, Yan Gao, null Yiwu, Yao Hu, Deyi Xiong
| Challenge: | Large language models suffer from multiple-file coding scenarios with strong inter-file dependencies . experimental results show that large language models exhibit inadequate performance in multi-file scenarios . |
| Approach: | They propose a retrieval-augmented reasoning framework for repository-level code repair . they use a dataset to generate standardized patches based on the key snippets . |
| Outcome: | The proposed framework improves retrieval accuracy and repair success on SWE-bench Lite . it surpasses models with larger size in managing extensive code contexts and fixing bugs spanning across multiple files. |
EcoLANG: Efficient and Effective Agent Communication Language Induction for Social Simulation (2025.findings-emnlp)
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| Challenge: | Large language models (LLMs) have demonstrated impressive ability to role-play humans and replicate complex social dynamics. |
| Approach: | They propose an efficient agent communication language induction for social simulations that reduces token consumption by over 20%. |
| Outcome: | The proposed model reduces token consumption by over 20% while preserving human language. |
Conditional Augmentation for Aspect Term Extraction via Masked Sequence-to-Sequence Generation (2020.acl-main)
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| Challenge: | Aspect term extraction is a task to extract aspect terms from review texts as opinion targets for sentiment analysis. |
| Approach: | They propose a conditional generation task for augmentation of aspect term extraction . they use a sequence-to-sequence method that generates a new sentence . results confirm that their method alleviates the data scarcity problem significantly . |
| Outcome: | The proposed method reduces the data scarcity problem significantly and boosts current models. |
LEANCODE: Understanding Models Better for Code Simplification of Pre-trained Large Language Models (2025.acl-long)
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| Challenge: | Large Language Models often require significant computational resources, often constraining input word or code token lengths. |
| Approach: | They propose to use the encoder-decoder attention scores to represent the importance of a code token across multiple contexts to reduce training and prediction time. |
| Outcome: | The proposed approach outperforms the SOTAs DietCode and SlimCode in code search and summarization tasks. |
Meet Changes with Constancy: Learning Invariance in Multi-Source Translation (2020.coling-main)
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| Challenge: | Existing approaches to multi-source neural machine translation neglect inconsistencies between sources of information. |
| Approach: | They propose a source invariance network to learn invariant information of parallel sources . they propose to integrate such network with multi-encoder based multi-source NMT methods . |
| Outcome: | The proposed approach achieves clear gains in translation quality and captures implicit invariance between different sources. |
Reading Like HER: Human Reading Inspired Extractive Summarization (D19-1)
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| Challenge: | Existing methods for extracting text summarization are abstractive and extractive. |
| Approach: | They propose a novel approach for extractive summarization by simulating two stages . they adopt a convolutional neural network to encode gist of paragraphs for rough reading . |
| Outcome: | The proposed method significantly outperforms the state-of-the-art extractive methods on CNN and DailyMail datasets. |
Vision-Language Pre-Training for Multimodal Aspect-Based Sentiment Analysis (2022.acl-long)
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| Challenge: | Existing approaches to multimodal Aspect-Based Sentiment Analysis (MABSA) ignore crossmodalalignment and use pre-trained visual and textual models. |
| Approach: | They propose a multimodal multimodal encoder-decoder framework for MABSA that uses a unified multimodal decoder architecture for all the pretrainingand downstream tasks. |
| Outcome: | The proposed framework outperforms state-of-the-art approaches on three MABSA subtasks. |
ASTRA: An Automated Framework for Strategy Discovery, Retrieval, and Evolution for Jailbreaking LLMs (2026.acl-long)
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| Challenge: | Existing methods lack the capability for continuous learning and self-evolution from interactions, limiting the diversity and adaptability of attack strategies. |
| Approach: | They propose an automated framework capable of discovering, retrieving, and evolving attack strategies. |
| Outcome: | The proposed framework outperforms existing baselines in a black-box setting. |