5.3.1 Insights from the SUPER Benchmark

5.3.1 Insights from the SUPER Benchmark

Evaluation of LLMs

• Assessing Ability to Reproduce and Execute Tasks:

  • Benchmark Testing: Use the SUPER benchmark to evaluate large language models (LLMs) like GPT-4 in setting up and executing tasks from research repositories.

  • Task Complexity Analysis: Examine how well models handle tasks of varying complexity, including multi-step processes and code modifications.

Key Findings

• Limitations Even in State-of-the-Art Models:

  • Performance Gaps: Even advanced models show limitations, solving only a fraction of complex tasks accurately.

  • Generalization Issues: Models struggle to apply learned knowledge to new, unseen problems.

• Struggles with Repository Comprehension and Task Setups:

  • Understanding Codebases: Difficulty in navigating and comprehending large code repositories.

  • Dependency Resolution: Challenges in managing dependencies and configurations required for task execution.