LLM4DS: Evaluating Large Language Models for Data Science Code Generation (2411.11908v1)

Abstract: The adoption of LLMs for code generation in data science offers substantial potential for enhancing tasks such as data manipulation, statistical analysis, and visualization. However, the effectiveness of these models in the data science domain remains underexplored. This paper presents a controlled experiment that empirically assesses the performance of four leading LLM-based AI assistants-Microsoft Copilot (GPT-4 Turbo), ChatGPT (o1-preview), Claude (3.5 Sonnet), and Perplexity Labs (Llama-3.1-70b-instruct)-on a diverse set of data science coding challenges sourced from the Stratacratch platform. Using the Goal-Question-Metric (GQM) approach, we evaluated each model's effectiveness across task types (Analytical, Algorithm, Visualization) and varying difficulty levels. Our findings reveal that all models exceeded a 50% baseline success rate, confirming their capability beyond random chance. Notably, only ChatGPT and Claude achieved success rates significantly above a 60% baseline, though none of the models reached a 70% threshold, indicating limitations in higher standards. ChatGPT demonstrated consistent performance across varying difficulty levels, while Claude's success rate fluctuated with task complexity. Hypothesis testing indicates that task type does not significantly impact success rate overall. For analytical tasks, efficiency analysis shows no significant differences in execution times, though ChatGPT tended to be slower and less predictable despite high success rates. This study provides a structured, empirical evaluation of LLMs in data science, delivering insights that support informed model selection tailored to specific task demands. Our findings establish a framework for future AI assessments, emphasizing the value of rigorous evaluation beyond basic accuracy measures.

• The paper demonstrates that LLMs achieve over 50% success, with ChatGPT and Claude surpassing 60% on many data science tasks.
• The paper employs a controlled, metric-driven experiment using 100 diverse Python coding problems to assess performance across analytical, algorithmic, and visualization tasks.
• The paper reveals trade-offs between accuracy and execution speed, indicating ChatGPT’s consistent performance in visualization tasks despite slower analytical processing.

Evaluating LLMs for Data Science Code Generation

The paper "LLM4DS: Evaluating LLMs for Data Science Code Generation" presents a methodical assessment of LLMs and their aptitude for generating code within the field of data science. The authors focus on four leading LLM-based AI assistants: Microsoft's GPT-4 Turbo, ChatGPT's o1-preview, Claude's 3.5 Sonnet, and Perplexity Labs' Llama-3.1-70b-instruct. The paper is comprehensive, employing systematically sourced data science coding problems from the Stratascratch platform, which presents significant challenges including data manipulation, statistical analysis, and visualization tasks.

Research Context and Objectives

This paper addresses the necessity of evaluating LLMs specifically for data science applications—a domain with distinct coding requirements. Previous research has primarily focused on general coding tasks and domain-specific applications in areas such as SQL generation. The paper employs a structured, empirical approach to bridge this gap, aiming to provide insights into the capability of LLMs in automating complex data science tasks.

Methodology

The paper utilizes a controlled experimental setup and the Goal-Question-Metric (GQM) approach to rigorously evaluate the models' performance across different data science task types (Analytical, Algorithm, Visualization) and levels of complexity (easy, medium, hard). A total of 100 Python coding problems were selected, encompassing a diverse range of themes within data science. By standardizing prompts and ensuring minimal interference with the LLM text generation process, the researchers ensure the accuracy of the results.

Key Findings and Analysis

1. Success Rates: All evaluated models achieve success rates surpassing a 50% baseline, indicating capability beyond stochastic guesswork. ChatGPT and Claude demonstrate performance significantly above a 60% threshold, yet no LLM reaches a 70% success rate, highlighting notable limits when meeting higher standards of accuracy. Interestingly, ChatGPT maintains consistent success across varied difficulties, whereas Claude's performance fluctuates with complexity, suggesting potential adaptability issues in complex task scenarios.
2. Task Type Influence: The paper's hypothesis testing reveals that the type of task (Analytical, Algorithm, or Visualization) does not significantly alter success rates for the models. Despite Claude and Perplexity showing variability in results corresponding to problem complexity, no model clearly outperforms others across all task types.
3. Performance Consistency and Efficiency: Although ChatGPT demonstrates slower and less predictable execution times in analytical tasks, it provides superior accuracy in visualization tasks. Claude and Perplexity Labs show comparable quality in image generation. This nuanced blend of strengths suggests trade-offs in selecting a model based on specific task requirements, balancing between execution speed and output accuracy.

Implications and Future Directions

The paper underscores the importance of comprehensive and tailored evaluation metrics for LLM performance in data science, expanding beyond simple accuracy. By establishing a structured benchmarking approach, it facilitates informed model selection aligned with specific data science objectives.

In future studies, expanding the dataset to encompass more diverse data science problems, including those involving embedded systems and real-time data analytics, would help further delineate the boundaries of LLM capabilities. Additionally, integrating more recent models and exploratory analyses involving even more specialized and computationally demanding tasks will offer deeper insight into their potential and limitations.

This research contributes significantly to advancing understanding of how LLMs can be effectively employed within data science, encouraging ongoing enhancement in AI model design and application for domain-specific tasks. As LLMs continue to develop, they hold significant promise in enhancing productivity and accessibility, particularly in fields requiring sophisticated data analysis and complex problem-solving skills.