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Automatic Semantic Augmentation of Language Model Prompts (for Code Summarization), (ICSE2024)
- Abstract: Large Language Models (LLM) are a new class of computation engines, "programmed" via prompt engineering. Researchers are still learning how to best "program" these LLMs to help developers. We start with the intuition that developers tend to consciously and unconsciously collect semantics facts, from the code, while working. Mostly these are shallow, simple facts arising from a quick read. For a function, such facts might include parameter and local variable names, return expressions, simple pre-...
- Labels: static analysis, code summarization, prompt strategy, retrieval-augmented generation
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DroidCoder: Enhanced Android Code Completion with Context-Enriched Retrieval-Augmented Generation, (ASE2024)
- Abstract: Android is the most popular mobile operating system. However, Android development requires extensive coding, especially for unique features such as lifecycle callbacks and UI widgets. Existing code completion methods typically utilize Retrieval-Augmented Generation (RAG) to provide contextual information for pre-trained code large language models (Code LLMs) to perform completion. Despite considerable progress in these methods, their effectiveness in Android development remains limited. This is ...
- Labels: code generation, code completion, prompt strategy, retrieval-augmented generation
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EvoR: Evolving Retrieval for Code Generation, (EMNLP2024)
- Abstract: Recently the retrieval-augmented generation (RAG) has been successfully applied in code generation. However, existing pipelines for retrieval-augmented code generation (RACG) employ static knowledge bases with a single source, limiting the adaptation capabilities of Large Language Models (LLMs) to domains they have insufficient knowledge of. In this work, we develop a novel pipeline, EVOR, that employs the synchronous evolution of both queries and diverse knowledge bases. On two realistic settin...
- Labels: code generation, code completion, source code model, prompt strategy, retrieval-augmented generation
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Instructive Code Retriever: Learn from Large Language Model's Feedback for Code Intelligence Tasks, (ASE2024)
- Abstract: Recent studies proposed to leverage large language models (LLMs) with In-Context Learning (ICL) to handle code intelligence tasks without fine-tuning. ICL employs task instructions and a set of examples as demonstrations to guide the model in generating accurate answers without updating its parameters. While ICL has proven effective for code intelligence tasks, its performance heavily relies on the selected examples. Previous work has achieved some success in using BM25 to retrieve examples for ...
- Labels: prompt strategy, retrieval-augmented generation
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Leandojo: Theorem proving with retrieval-augmented language models, (NeurIPS2023)
- Abstract: Large language models (LLMs) have shown promise in proving formal theorems using proof assistants such as Lean. However, existing methods are difficult to reproduce or build on, due to private code, data, and large compute requirements. This has created substantial barriers to research on machine learning methods for theorem proving. This paper removes these barriers by introducing LeanDojo: an open-source Lean playground consisting of toolkits, data, models, and benchmarks. LeanDojo extracts da...
- Labels: prompt strategy, retrieval-augmented generation
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Repository-Level Prompt Generation for Large Language Models of Code, (ICML2023)
- Abstract: With the success of large language models (LLMs) of code and their use as code assistants (e.g. Codex used in GitHub Copilot), techniques for introducing domain-specific knowledge in the prompt design process become important. In this work, we propose a framework called Repo-Level Prompt Generator that learns to generate example-specific prompts using prompt proposals. The prompt proposals take context from the entire repository, thereby incorporating both the structure of the repository and the...
- Labels: code generation, code completion, prompt strategy, retrieval-augmented generation
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Retrieval-Based Prompt Selection for Code-Related Few-Shot Learning, (ICSE2023)
- Abstract: Large language models trained on massive code corpora can generalize to new tasks without the need for task-specific fine-tuning. In few-shot learning, these models take as input a prompt, composed of natural language instructions, a few instances of task demonstration, and a query and generate an output. However, the creation of an effective prompt for code-related tasks in few-shot learning has received little attention. We present a technique for prompt creation that automatically retrieves c...
- Labels: general coding task, code model, code model training, prompt strategy, retrieval-augmented generation