EVOR: Evolving Retrieval for Code Generation
This paper presents a new method for improving how computers generate code by using a more dynamic approach to gather information. Instead of relying on fixed sources of knowledge, the method adapts and evolves based on feedback from code execution.
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- 1 The objective of retrieval-augmented code generation is to first retrieve relevant information from external knowledge and then augment large language models to generate a program in a target library or programming language that the LLM is not familiar with.
- 2 We investigate the efficacy of LLMs in utilizing web search content to solve unfamiliar coding problems without further training.
- 3 Code snippets in programming language naturally align with the LLM generation objective and provide concrete examples of inputs, outputs, and parameters.
- 4 Code snippets naturally align with the LLM generation objective and provide concrete examples of inputs, outputs, and parameters.
Introduction
Recent research has demonstrated successful applications of RAG in code generation. They implement retrieval-augmented code generation (RACG) pipelines using a given query or a rewritten version to retrieve from a static knowledge base with a single type of information.
More knowledge sources are potentially helpful to generalization.
The unique characteristic of execution in code generation enables more information collected on-the-fly.
Despite the effectiveness of EVOR in RACG, one limitation is that it requires multiple rounds of interactions among retrievers, LLMs, and executors to output the code answer.
Although LLMs cannot guarantee to write accurate test cases, their performance in generating only program inputs is exceptionally high.
Research Question
The objective of retrieval-augmented code generation is to first retrieve relevant information from external knowledge and then augment large language models to generate a program in a target library or programming language that the LLM is not familiar with. We investigate the efficacy of LLMs in utilizing web search content to solve unfamiliar coding problems without further training.
Code snippets in programming language naturally align with the LLM generation objective and provide concrete examples of inputs, outputs, and parameters.
Code snippets naturally align with the LLM generation objective and provide concrete examples of inputs, outputs, and parameters.
Methodology
This information is easily obtained and can enrich knowledge bases shared among all instances of the same task. Experimental results across these four datasets demonstrate that our method yields a significant improvement in the average performance over existing code generation methods.
Study Design
Further analysis unveils that both synchronous evolution and diverse sources in knowledge bases are critical to the success of EVOR.
Our task reflects a more realistic yet challenging scenario for LLMs.
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Results & Findings
The retrieval-augmented generation (RAG) paradigm has raised significant attention due to its efficiency in adapting large language models (LLMs) without training. A successfully executed code snippet generated by LLMs is guaranteed to be syntactically correct and can serve as a concrete example to demonstrate the corresponding grammar or function usage.
- The retrieval-augmented generation (RAG) paradigm has raised significant attention due to its efficiency in adapting large language models (LLMs) without training.
- A successfully executed code snippet generated by LLMs is guaranteed to be syntactically correct and can serve as a concrete example to demonstrate the corresponding grammar.
- This strategic refinement aims to facilitate the extraction of the most pertinent information.
- We compile a new benchmark, EVOR-BENCH, comprising four datasets designed to simulate realistic scenarios in RACG to prevent data leakage and assess EVOR under a reliable.
- The remaining two datasets simulate the introduction of new grammars with the help of two less-common programming languages, Ring and Pony.
A successfully executed code snippet generated by LLMs is guaranteed to be syntactically correct and can serve as a concrete example to demonstrate the corresponding grammar or function usage.
Practical Applications
The general web search may not provide the most effective information to adapt LLMs in RACG. Different from the documentation in EVOR-BENCH, the repository code could be much more complex with intertwined variable dependencies, customized function calls, etc.
There is a risk of biased or incorrect information being retrieved, which could propagate errors or introduce vulnerabilities into generated code.
For instance, we may add 1 or subtract 1 from an integer to mutate it.
Evolving Retrieval
This section outlines the process of retrieval-augmented code generation, emphasizing the synchronous evolution of queries and knowledge bases to enhance the retrieval model’s ability to identify relevant information, thereby improving LLM output quality.
Query evolution
Query evolution describes the iterative process of refining queries based on execution feedback and LLM outputs. It details how initial queries are transformed through multiple iterations to retrieve more relevant knowledge for code generation.
Figures Explained
Frequently Asked Questions
The objective of retrieval-augmented code generation is to first retrieve relevant information from external knowledge and then augment large language models to generate a program in a target library or programming language that the LLM is not familiar with. We investigate the.
This information is easily obtained and can enrich knowledge bases shared among all instances of the same task. Further analysis unveils that both synchronous evolution and diverse sources in knowledge bases are critical to the success of EVOR.
A successfully executed code snippet generated by LLMs is guaranteed to be syntactically correct and can serve as a concrete example to demonstrate the corresponding grammar or function usage. We curate a new benchmark to evaluate the generalization capability of LLMs with.
There is a risk of biased or incorrect information being retrieved, which could propagate errors or introduce vulnerabilities into generated code. Although our system still looks to be effective in their benchmarks with performance gain by including more knowledge sources, we are.
Despite the effectiveness of EVOR in RACG, one limitation is that it requires multiple rounds of interactions among retrievers, LLMs, and executors to output the code answer. Although LLMs cannot guarantee to write accurate test cases, their performance in generating only program.
This paper presents a new method for improving how computers generate code by using a more dynamic approach to gather information. Instead of relying on fixed sources of knowledge, the method adapts and evolves based on feedback from code execution.