ML-Bench: Evaluating Large Language Models and Agents for Machine Learning Tasks on Repository-Level Code

TL;DR

ML-Bench is a comprehensive benchmark designed to evaluate large language models and AI agents on real-world, repository-level coding tasks, highlighting current capabilities and areas for improvement in understanding complex code interactions.

Contribution

The paper introduces ML-Bench, a novel benchmark with real-world code examples for evaluating LLMs and agents on repository-scale code understanding and execution tasks.

Findings

GPT-4o achieves over 50% Pass@5 in code generation tasks.

GPT-4o attains a 76.47% success rate in autonomous agent tasks.

Significant challenges remain, including hallucinations and bash script generation issues.

Abstract

Despite Large Language Models (LLMs) like GPT-4 achieving impressive results in function-level code generation, they struggle with repository-scale code understanding (e.g., coming up with the right arguments for calling routines), requiring a deeper comprehension of complex file interactions. Also, recently, people have developed LLM agents that attempt to interact with repository code (e.g., compiling and evaluating its execution), prompting the need to evaluate their performance. These gaps have motivated our development of ML-Bench, a benchmark rooted in real-world programming applications that leverage existing code repositories to perform tasks. Addressing the need for LLMs to interpret long code contexts and translate instructions into precise, executable scripts, ML-Bench encompasses annotated 9,641 examples across 18 GitHub repositories, challenging LLMs to accommodate user-specified arguments and documentation intricacies effectively. To evaluate both 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. Our findings indicate that while GPT-4o leads with a Pass@5 rate surpassing 50%, there remains significant scope for improvement, highlighted by issues such as hallucinated outputs and difficulties with bash script generation. Notably, in the more demanding ML-Agent-Bench, GPT-4o achieves a 76.47% success rate, reflecting the efficacy of iterative action and feedback in complex task resolution. Our code, dataset, and models are available at https://github.com/gersteinlab/ML-bench.