Linguacodus: A Synergistic Framework for Transformative Code Generation in Machine Learning Pipelines

Abstract

In the ever-evolving landscape of machine learning, seamless translation of natural language descriptions into executable code remains a formidable challenge. This paper introduces Linguacodus, an innovative framework designed to tackle this challenge by deploying a dynamic pipeline that iteratively transforms natural language task descriptions into code through high-level data-shaping instructions. The core of Linguacodus is a fine-tuned large language model (LLM), empowered to evaluate diverse solutions for various problems and select the most fitting one for a given task. This paper details the fine-tuning process, and sheds light on how natural language descriptions can be translated into functional code. Linguacodus represents a substantial leap towards automated code generation, effectively bridging the gap between task descriptions and executable code. It holds great promise for advancing machine learning applications across diverse domains. Additionally, we propose an algorithm capable of transforming a natural description of an ML task into code with minimal human interaction. In extensive experiments on a vast machine learning code dataset originating from Kaggle, we showcase the effectiveness of Linguacodus. The investigations highlight its potential applications across diverse domains, emphasizing its impact on applied machine learning in various scientific fields.

References in corpus (19)

• Llama 2: Open Foundation and Fine-Tuned Chat Models
• PaLM: Scaling Language Modeling with Pathways
• Sparks of Artificial General Intelligence: Early experiments with GPT-4
• Evaluating Large Language Models Trained on Code
• CTRL: A Conditional Transformer Language Model for Controllable Generation
• Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena
• Code Llama: Open Foundation Models for Code
• HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Face
• CodeBERT: A Pre-Trained Model for Programming and Natural Languages
• PaLM 2 Technical Report
• Texygen: A Benchmarking Platform for Text Generation Models
• A Syntactic Neural Model for General-Purpose Code Generation
• Latent Predictor Networks for Code Generation
• Abstract Syntax Networks for Code Generation and Semantic Parsing
• Training and Evaluating a Jupyter Notebook Data Science Assistant
• TRANX: A Transition-based Neural Abstract Syntax Parser for Semantic Parsing and Code Generation
• DERA: Enhancing Large Language Model Completions with Dialog-Enabled Resolving Agents
• Natural Language to Code Generation in Interactive Data Science Notebooks
• Grounding Data Science Code Generation with Input-Output Specifications