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        <title>On Improving Repository-Level Code QA for Large Language Models</title>
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            <namePart type="given">Xiyan</namePart>
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    <abstract>Large Language Models (LLMs) such as ChatGPT, GitHub Copilot, Llama, or Mistral assist programmers as copilots and knowledge sources to make the coding process faster and more efficient. This paper aims to improve the copilot performance by implementing different self-alignment processes and retrieval-augmented generation (RAG) pipelines, as well as their combination. To test the effectiveness of all approaches, we create a dataset and apply a model-based evaluation, using LLM as a judge. It is designed to check the model’s abilities to understand the source code semantics, the dependency between files, and the overall meta-information about the repository. We also compare our approach with other existing solutions, e.g. ChatGPT-3.5, and evaluate on the existing benchmarks. Code and dataset are available online (https://anonymous.4open.science/r/ma_llm-382D).</abstract>
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%0 Conference Proceedings
%T On Improving Repository-Level Code QA for Large Language Models
%A Strich, Jan
%A Schneider, Florian
%A Nikishina, Irina
%A Biemann, Chris
%Y Fu, Xiyan
%Y Fleisig, Eve
%S Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop)
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F strich-etal-2024-improving
%X Large Language Models (LLMs) such as ChatGPT, GitHub Copilot, Llama, or Mistral assist programmers as copilots and knowledge sources to make the coding process faster and more efficient. This paper aims to improve the copilot performance by implementing different self-alignment processes and retrieval-augmented generation (RAG) pipelines, as well as their combination. To test the effectiveness of all approaches, we create a dataset and apply a model-based evaluation, using LLM as a judge. It is designed to check the model’s abilities to understand the source code semantics, the dependency between files, and the overall meta-information about the repository. We also compare our approach with other existing solutions, e.g. ChatGPT-3.5, and evaluate on the existing benchmarks. Code and dataset are available online (https://anonymous.4open.science/r/ma_llm-382D).
%U https://aclanthology.org/2024.acl-srw.28
%P 303-338

Markdown (Informal)

[On Improving Repository-Level Code QA for Large Language Models](https://aclanthology.org/2024.acl-srw.28) (Strich et al., ACL 2024)

• On Improving Repository-Level Code QA for Large Language Models (Strich et al., ACL 2024)

ACL

• Jan Strich, Florian Schneider, Irina Nikishina, and Chris Biemann. 2024. On Improving Repository-Level Code QA for Large Language Models. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop), pages 303–338, Bangkok, Thailand. Association for Computational Linguistics.