| Challenge: | Existing approaches to automatically generate commit messages are repetitive or redundant. |
| Approach: | They propose a retrieval-augmented neural commit message generation method which treats the retrieved similar commit as an exemplar and leverages it to generate an accurate commit message. |
| Outcome: | The proposed method outperforms baselines on a large dataset with five programming languages and can boost existing Seq2Seq models in commit message generation. |
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Leveraging Context-Aware Prompting for Commit Message Generation (2024.emnlp-main)
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| Challenge: | Existing methods for writing comprehensive commit messages focus on the changed lines or nearest context lines, but excessive contexts can lead to noise. |
| Approach: | They propose a code model COMMIT that can generate automatic commit messages by combining a dataset with a context-aware prompt. |
| Outcome: | The proposed model surpasses all existing models including pre-trained language models for code and large language models such as Code-LlaMa. |
SelfRACG: Enabling LLMs to Self-Express and Retrieve for Code Generation (2025.emnlp-main)
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Qian Dong, Jia Chen, Qingyao Ai, Hongning Wang, Haitao Li, null Yiwu, Yao Hu, Yiqun Liu, Shaoping Ma
| Challenge: | Existing retrieval-augmented code generation methods fail to accurately fetch the knowledge required for code generation for consecutive code fragments. |
| Approach: | They propose a paradigm that enables large language models to Self-express their information needs to enhance retrieval-augmented code generation methods. |
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ReACC: A Retrieval-Augmented Code Completion Framework (2022.acl-long)
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| Challenge: | Recent work has shown that statistical language modeling with transformers can greatly improve the performance in code completion tasks. |
| Approach: | They propose a retrieval-augmented code completion framework that combines a source code retriever and an auto-regressive language model for programming language. |
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EvoR: Evolving Retrieval for Code Generation (2024.findings-emnlp)
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| Challenge: | Existing pipelines for retrieval-augmented code generation (RACG) use static knowledge bases with a single source, limiting adaptation capabilities of Large Language Models (LLMs) Extensive experiments demonstrate that EVOR achieves two to four times of execution accuracy compared to other methods such as Reflexion. |
| Approach: | They propose a retrieval-augmented code generation pipeline that employs the synchronous evolution of queries and diverse knowledge bases. |
| Outcome: | The proposed pipeline achieves two to four times of execution accuracy compared to other methods. |
Retrieval-augmented Generation across Heterogeneous Knowledge (2022.naacl-srw)
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| Challenge: | Existing methods for retrieving knowledge from a single source homogeneous corpus have been gaining increasing attention in the field of natural language processing (NLP) however, they still suffer from the following drawbacks: (i) They are usually trained offline, making the model agnostic to the latest information, e.g., asking a chat-bot about COVID-19. |
| Approach: | They propose to use a single-source homogeneous corpus to generate retrieval-augmented generation models that can learn from the pre-training corpus. |
| Outcome: | The proposed methods have been applied to various knowledge-intensive NLP tasks, but most of the work has focused on retrieving unstructured text documents from Wikipedia. |
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. |
tRAG: Term-level Retrieval-Augmented Generation for Domain-Adaptive Retrieval (2025.naacl-long)
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| Challenge: | Neural retrieval models suffer when there is a domain shift between training and test data distributions. |
| Approach: | They propose to generate domain-adapted pseudo-queries using large language models (LLMs) to improve term recall of unseen query terms by using term-level Retrieval-Augmented Generation (tRAG). |
| Outcome: | The proposed method significantly improves recall for unseen terms by 10.6% and outperforms LLM and retrieval-augmented generation baselines on overall retrieval performance. |
Retrieval Enhancements for RAG: Insights from a Deployed Customer Support Chatbot (2026.eacl-industry)
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Daniel González Juclà, Mohit Tuteja, Marcos Esteve Casademunt, Keshav Unnikrishnan, Yasir Usmani, Arvind Roshaan
| Challenge: | a persistent gap remains between Recall@10 and Recall @50 across datasets . |
| Approach: | They evaluate embedding model comparison, Reciprocal Rank Fusion and embedded concatenation techniques to improve retrieval quality. |
| Outcome: | The proposed methods outperform traditional cross-encoders in identifying high-relevance passages. |
RepoCoder: Repository-Level Code Completion Through Iterative Retrieval and Generation (2023.emnlp-main)
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Fengji Zhang, Bei Chen, Yue Zhang, Jacky Keung, Jin Liu, Daoguang Zan, Yi Mao, Jian-Guang Lou, Weizhu Chen
| Challenge: | RepoCoder is a repository-level code completion framework that utilizes the useful information scattered in files. |
| Approach: | They propose a repository-level code completion framework called RepoCoder . it integrates a similarity-based retriever and a pre-trained code language model . they propose 'repoBench' benchmark to validate the framework's effectiveness . |
| Outcome: | The proposed framework outperforms the vanilla retrieval-augmented code completion approach in the real-world. |
Jointly Learning to Repair Code and Generate Commit Message (2021.emnlp-main)
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| Challenge: | Existing work performs code repair and commit message generation independently. |
| Approach: | They propose a cascaded method to repair program codes and generate commit messages in a unified framework. |
| Outcome: | The proposed model significantly outperforms baselines on a buggy-fixed-commit dataset. |