Code Generation with AlphaCodium: From Prompt Engineering to Flow Engineering 摘要 代码生成和常规的自然语言的任务不同,它们需要匹配目标编程语言的确切语法,识别happy paths和edge cases ,关注问题描述的大量的具体细节,处理一些代码特定的需求和issue。 然而,许多在自然语言上表现得很好的优化和trick在代码相关的任...
原文总结:In conclusion, OpenCodeInterpreter represents a significant leap forward in the field of code generation, bridging the previously identified gap between open-source models and the advanced capabilities of proprietary systems like the GPT-4 Code Interpreter. By integrating compiler diagnostics and...
Programmers frequently engage with machine learning tutorials in computational notebooks and have been adopting code generation technologies based on large language models (LLMs). However, they encounter difficulties in understanding and working with code produced by LLMs. To mitigate these challenges, we...
Since its inception in mid-2021, the HumanEval benchmark has not only become immensely popular but has also emerged as a quintessential evaluation tool for measuring the performance of LLMs in code generation tasks. The [leaderboard](https://paperswithcode.com/sota/code-generation-on-humaneval...
Developers can guard against this risk with the following best practices: Always check LLM inputs.Code generation tools are great for simple development tasks like creating boilerplate code and API handlers. However, you also need input validation and guardrails to constrain LLM-based apps. These ...
针对NL2Code 任务对 27 个具有代表性的 LLMs 进行了全面调研,下表总结了每个模型的详细信息,其中主要包括:模型架构、模型大小、模型层数(L)、注意力头数量(A)、隐藏维度(H)、模型参数是否开放(P)等五个方面。 为了更好地可视化,下图按时间顺序展示了这些模型,绘制了最大的模型大小。观察到的一个趋势是,随着研...
Official implementation for the paper: "Code Generation with AlphaCodium: From Prompt Engineering to Flow Engineering"" - Codium-ai/AlphaCodium
code-generationpaper-implementationsstate-of-the-artflow-engineeringbroader-impacts UpdatedNov 25, 2024 Python Your agent in your terminal, equipped with local tools: writes code, uses the terminal, browses the web, vision. clichatbotopenaicode-generationai-agentsragai-assistantllmchatgptanthropicllama...
Prompt, language choice and code generation The benchmark was designed this way: LLM makes only one attempt to generate code without any prior information about the problem (or any other problems) and without knowing its test cases, except those that were in the description itself. There is no...
distinctive features from the personal context into concise, descriptive sentences, precisely tailoring their generation more closely to an individual's unique habits and preferences. Our experimental results show that GPG improves LLM's personalization ability across different tasks, for example, it increa...