Prompt Alchemy: Automatic Prompt Refinement for Enhancing Code Generation
- URL: http://arxiv.org/abs/2503.11085v1
- Date: Fri, 14 Mar 2025 04:53:03 GMT
- Title: Prompt Alchemy: Automatic Prompt Refinement for Enhancing Code Generation
- Authors: Sixiang Ye, Zeyu Sun, Guoqing Wang, Liwei Guo, Qingyuan Liang, Zheng Li, Yong Liu,
- Abstract summary: Prochemy is an innovative method for automatically refining prompts to boost code generation.<n>It iteratively refines prompts based on model performance, using an optimized final prompt for improved consistency across tasks.<n>For code translation, Prochemy boosts GPT-4o's Java-to-Python (AVATAR) performance from 74.5 to 84.1 (+12.9%) and Python-to-Java from 66.8 to 78.2 (+17.1%)
- Score: 19.745848581060528
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Code generation has emerged as a key task to automate software development by converting high-level descriptions into executable code. Large language models (LLMs) excel at this but depend heavily on input prompt quality.Manual prompt engineering can be time-consuming and inconsistent, limiting LLM effectiveness. This paper introduces Prochemy, an innovative method for automatically refining prompts to boost code generation. Prochemy overcomes manual prompt limitations by automating optimization, ensuring consistency during inference, and supporting multi-agent systems.It iteratively refines prompts based on model performance, using an optimized final prompt for improved consistency across tasks. We tested Prochemy on natural language-based code generation and translation tasks using three LLM series. Results indicate Prochemy enhances existing methods, improving performance by 5.0% for GPT-3.5-Turbo and 1.9% for GPT-4o over zero-shot baselines on HumanEval. In state-of-the-art LDB, Prochemy + LDB surpasses standalone methods by 1.2-1.8%. For code translation, Prochemy boosts GPT-4o's Java-to-Python (AVATAR) performance from 74.5 to 84.1 (+12.9%) and Python-to-Java from 66.8 to 78.2 (+17.1%). Moreover, Prochemy maintains strong performance when integrated with the o1-mini model, validating its efficacy in code tasks. Designed as plug-and-play, Prochemy optimizes prompts with minimal human input, bridging the gap between simple prompts and complex frameworks.
Related papers
- PerfCodeGen: Improving Performance of LLM Generated Code with Execution Feedback [78.89596149768458]
Large Language Models (LLMs) are widely adopted for assisting in software development tasks.<n>We propose PerfCodeGen, a training-free framework that enhances the performance of LLM-generated code.
arXiv Detail & Related papers (2024-11-18T06:22:38Z) - Large Language Models as Code Executors: An Exploratory Study [29.545321608864295]
This paper pioneers the exploration of Large Language Models (LLMs) as code executors.
We are the first to examine this feasibility across various LLMs, including OpenAI's o1, GPT-4o, GPT-3.5, DeepSeek, and Qwen-Coder.
We introduce an Iterative Instruction Prompting (IIP) technique that processes code snippets line by line, enhancing the accuracy of weaker models by an average of 7.22%.
arXiv Detail & Related papers (2024-10-09T08:23:22Z) - Automated Prompt Engineering for Cost-Effective Code Generation Using Evolutionary Algorithm [8.009881267479189]
Large Language Models have seen increasing use in various software development tasks, especially in code generation.<n>We propose an alternative approach named Evolutionary Prompt Engineering for Code (EPiC)<n>EPiC uses a lightweight evolutionary algorithm to refine the original prompts into improved versions that generate high quality code.<n>Our evaluation against state-of-the-art (SOTA) LLM based code generation agents shows that EPiC not only achieves up to 6% improvement in pass@k but is also 2-10 times more cost-effective than the baselines.
arXiv Detail & Related papers (2024-08-20T21:15:36Z) - Navigating the Labyrinth: Evaluating and Enhancing LLMs' Ability to Reason About Search Problems [59.72548591120689]
We introduce a new benchmark, SearchBench, containing 11 unique search problem types.
We show that even the most advanced LLMs fail to solve these problems end-to-end in text.
Instructing LLMs to generate code that solves the problem helps, but only slightly, e.g., GPT4's performance rises to 11.7%.
arXiv Detail & Related papers (2024-06-18T00:44:58Z) - Supervisory Prompt Training [2.0431551512846244]
We propose a novel approach, Supervisory Prompt Training (SPT)
SPT automates the generation of highly effective prompts using a dual Large Language Models (LLMs) system.
In this system, one LLM, the generator, performs a task while the other, the corrector, provides feedback and generates improved prompts.
arXiv Detail & Related papers (2024-03-26T19:08:20Z) - Unleashing the Potential of Large Language Models as Prompt Optimizers: Analogical Analysis with Gradient-based Model Optimizers [108.72225067368592]
We propose a novel perspective to investigate the design of large language models (LLMs)-based prompts.
We identify two pivotal factors in model parameter learning: update direction and update method.
We develop a capable Gradient-inspired Prompt-based GPO.
arXiv Detail & Related papers (2024-02-27T15:05:32Z) - PRompt Optimization in Multi-Step Tasks (PROMST): Integrating Human Feedback and Heuristic-based Sampling [20.0605311279483]
We introduce PRompt Optimization in Multi-Step Tasks (PROMST)
It incorporates human-designed feedback rules to automatically offer direct suggestions for improvement.
It significantly outperforms both human-engineered prompts and several other prompt optimization methods across 11 representative multi-step tasks.
arXiv Detail & Related papers (2024-02-13T16:38:01Z) - ML-Bench: Evaluating Large Language Models and Agents for Machine Learning Tasks on Repository-Level Code [76.84199699772903]
ML-Bench is a benchmark rooted in real-world programming applications that leverage existing code repositories to perform tasks.
To evaluate both Large Language Models (LLMs) and AI agents, two setups are employed: ML-LLM-Bench for assessing LLMs' text-to-code conversion within a predefined deployment environment, and ML-Agent-Bench for testing autonomous agents in an end-to-end task execution within a Linux sandbox environment.
arXiv Detail & Related papers (2023-11-16T12:03:21Z) - Instances Need More Care: Rewriting Prompts for Instances with LLMs in the Loop Yields Better Zero-Shot Performance [11.595274304409937]
Large language models (LLMs) have revolutionized zero-shot task performance.
Current methods using trigger phrases such as "Let's think step by step" remain limited.
This study introduces PRomPTed, an approach that optimize the zero-shot prompts for individual task instances.
arXiv Detail & Related papers (2023-10-03T14:51:34Z) - Connecting Large Language Models with Evolutionary Algorithms Yields
Powerful Prompt Optimizers [70.18534453485849]
EvoPrompt is a framework for discrete prompt optimization.
It borrows the idea of evolutionary algorithms (EAs) as they exhibit good performance and fast convergence.
It significantly outperforms human-engineered prompts and existing methods for automatic prompt generation.
arXiv Detail & Related papers (2023-09-15T16:50:09Z) - Large Language Models as Optimizers [106.52386531624532]
We propose Optimization by PROmpting (OPRO), a simple and effective approach to leverage large language models (LLMs) as prompts.
In each optimization step, the LLM generates new solutions from the prompt that contains previously generated solutions with their values.
We demonstrate that the best prompts optimized by OPRO outperform human-designed prompts by up to 8% on GSM8K, and by up to 50% on Big-Bench Hard tasks.
arXiv Detail & Related papers (2023-09-07T00:07:15Z) - Progressive-Hint Prompting Improves Reasoning in Large Language Models [63.98629132836499]
This paper proposes a new prompting method, named Progressive-Hint Prompting (PHP)
It enables automatic multiple interactions between users and Large Language Models (LLMs) by using previously generated answers as hints to progressively guide toward the correct answers.
We conducted extensive and comprehensive experiments on seven benchmarks. The results show that PHP significantly improves accuracy while remaining highly efficient.
arXiv Detail & Related papers (2023-04-19T16:29:48Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.