Papers by Yiqing Xie
SACL: Understanding and Combating Textual Bias in Code Retrieval with Semantic-Augmented Reranking and Localization (2025.findings-emnlp)
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| Challenge: | a recent study shows that code retrievers exhibit a strong bias towards well-documented code . |
| Approach: | They propose a framework that augments textual information with semantic information to mask specific features while preserving code functionality. |
| Outcome: | The proposed framework enhances textual information and reduces bias by augmenting code or structural knowledge with semantic information. |
DocLens: Multi-aspect Fine-grained Medical Text Evaluation (2024.acl-long)
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Yiqing Xie, Sheng Zhang, Hao Cheng, Pengfei Liu, Zelalem Gero, Cliff Wong, Tristan Naumann, Hoifung Poon, Carolyn Rose
| Challenge: | Medical text generation systems are widely used to assist with administrative work and highlight salient information to support decision-making. |
| Approach: | They propose a set of metrics to evaluate completeness, conciseness, and attribution of medical text at a fine-grained level. |
| Outcome: | The proposed framework exhibits substantially higher agreement with medical experts than existing metrics. |
Eider: Empowering Document-level Relation Extraction with Efficient Evidence Extraction and Inference-stage Fusion (2022.findings-acl)
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| Challenge: | Document-level relation extraction (DocRE) aims to extract semantic relations among entity pairs in a document. |
| Approach: | They propose an evidence-enhanced framework that empowers document-level relation extraction (DocRE) Eider efficiently extracts evidence and effectively fuses extracted evidence in inference. |
| Outcome: | The proposed framework outperforms state-of-the-art methods on three benchmark datasets. |
Improving Model Factuality with Fine-grained Critique-based Evaluator (2025.acl-long)
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Yiqing Xie, Wenxuan Zhou, Pradyot Prakash, Di Jin, Yuning Mao, Quintin Fettes, Arya Talebzadeh, Sinong Wang, Han Fang, Carolyn Rose, Daniel Fried, Hejia Zhang
| Challenge: | Factuality evaluation aims to detect factual errors produced by language models and guide the development of more factual models. |
| Approach: | They propose a framework that leverages FenCE to improve the factuality of LM generators by constructing training data. |
| Outcome: | The proposed framework improves the factuality of LM generators by enhancing their training data. |
Multi-document Summarization with Maximal Marginal Relevance-guided Reinforcement Learning (2020.emnlp-main)
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| Challenge: | Recent studies on single-document summarization (SDS) benefit from advances in neural sequence learning, but they produce unsatisfactory results on multi-document summary (MDS). |
| Approach: | They propose a neural sequence learning method that unifies advanced neural SDS methods and statistical measures used in classical MDS. |
| Outcome: | The proposed method achieves state-of-the-art performance on benchmark MDS datasets. |
An Empirical Study on Strong-Weak Model Collaboration for Repo-level Code Generation (2025.emnlp-main)
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| Challenge: | Recent advances in large language models (LLMs) have demonstrated impressive capabilities across complex reasoning and generation tasks. |
| Approach: | They evaluate a broad spectrum of collaboration strategies for repository-level code generation where the weak model handles simpler tasks at lower cost and the most challenging tasks are delegated to the strong model. |
| Outcome: | The proposed model achieves equivalent performance to the strong model while reducing the cost by 40%. |
Model-Generated Pretraining Signals Improves Zero-Shot Generalization of Text-to-Text Transformers (2023.acl-long)
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Linyuan Gong, Chenyan Xiong, Xiaodong Liu, Payal Bajaj, Yiqing Xie, Alvin Cheung, Jianfeng Gao, Xia Song
| Challenge: | Recent work in NLP has shown that pretrained language models have made notable progress toward generalization to unseen tasks. |
| Approach: | They propose to pretrain T5 using an auxiliary model to construct more challenging token replacements for the main model to denoise. |
| Outcome: | The proposed model outperforms similar-sized baseline models on prompted NLP benchmarks and rivals the state-of-the-art model with only **8%** of its parameters. |
CodeRAG-Bench: Can Retrieval Augment Code Generation? (2025.findings-naacl)
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Zora Zhiruo Wang, Akari Asai, Xinyan Velocity Yu, Frank F. Xu, Yiqing Xie, Graham Neubig, Daniel Fried
| Challenge: | Language models excel at generating code, but many programs are difficult to generate using only parametric knowledge. |
| Approach: | They propose a retrieval-augmented code generation benchmark that provides reproducible evaluations on retrieval and end-to-end code generation performance. |
| Outcome: | The proposed benchmark covers programming, open-domain, and repository-level tasks and provides reproducible evaluations on retrieval and end-to-end code generation performance. |
Data Augmentation for Code Translation with Comparable Corpora and Multiple References (2023.findings-emnlp)
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| Challenge: | Existing methods for translating code between programming languages are limited by parallel training data. |
| Approach: | They propose a data augmentation technique that builds comparable corpora and augments existing parallel data with multiple reference translations. |
| Outcome: | The proposed techniques improve CodeT5 translation between Java, Python, and C++ by an average of 7.5% Computational Accuracy (CA@1) . |
Open-Vocabulary Argument Role Prediction For Event Extraction (2022.findings-emnlp)
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| Challenge: | Existing studies on event extraction depend on pre-defined argument roles . despite great progress, many studies still rely on hand-crafted ontologies . |
| Approach: | They propose an unsupervised framework for customizing argument roles for event extraction . they propose a human-annotated event extraction dataset with 143 customized argument roles . |
| Outcome: | The proposed framework outperforms existing methods on an event extraction dataset. |