LLMs Struggle to Replicate Research Results: New Benchmark Unveils Challenges
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Research Results Are Difficult to Replicate
A core principle of science is reproducibility. Yet, replicating research findings in machine learning (ML) and natural language processing (NLP) often proves challenging. This is because research code is frequently difficult to run, requiring substantial effort to set up and execute. Researchers often face difficulties like installing the correct environment, resolving outdated dependencies, fixing bugs, and determining the correct execution commands. These steps are especially time-consuming, as support and documentation may be incomplete or even unavailable.
Can LLMs Automate This Process?
Recent progress in large language models (LLMs) has led to advancements in automating code tasks, such as writing code, editing code, and even resolving GitHub issues. But can LLMs go beyond these specific tasks and autonomously replicate research results from existing repositories?
SUPER: Evaluating Agents on Setting Up and Executing Tasks from Research Repositories
To assess the capability of LLMs to perform these tasks, researchers at the Allen Institute for AI have developed a new benchmark called SUPER (Setting UP and Executing tasks from Research repositories). SUPER aims to capture the realistic challenges faced by researchers working with ML and NLP research repositories.
Three Distinct Problem Sets
The benchmark consists of three distinct problem sets:
- Expert Set: Contains 45 manually curated problems solved by experts. Each task involves reproducing a research result from a specific repository.
- Masked Set: Includes 152 sub-problems derived from the Expert set by removing specific parts of the expert-written code. Each sub-problem targets a particular challenge, such as installing dependencies, configuring experimental data, setting up hyperparameters, resolving runtime exceptions, or correctly executing scripts.
- Auto Set: Contains 604 automatically generated problems, designed for larger-scale development and fine-tuning, using environment feedback.
Challenges Faced by LLMs
The results from the benchmark highlight the significant challenges faced by current LLM-based agents in solving these tasks:
- Sub-problems vs. End-to-End Tasks: LLMs perform better at resolving well-specified sub-problems, such as solving exceptions, bugs, and other issues, than at tasks requiring exploration of the repository’s code structure to understand the code’s logic.
- Low-Profile Repositories: Even for the most advanced models like GPT-4, the success rate is low (only 16.3% of end-to-end tasks and 46.1% of the masked sub-problems). This suggests that current LLMs struggle to tackle tasks involving less-popular repositories that are not well-documented or maintained.
- Open-Source Models: The performance of open-source models is significantly behind that of proprietary models.
SUPER: A Valuable Resource for the Research Community
SUPER serves as a valuable resource for the research community:
- Evaluating Existing Agents: It provides a comprehensive benchmark to evaluate the performance of LLM-based agents in setting up and executing tasks from research repositories.
- Encouraging Progress: It highlights the challenges in this domain and provides a platform for researchers to make progress in developing more robust and effective LLM agents.
Conclusion
While LLMs have made significant progress in code-related tasks, SUPER reveals that autonomously replicating research results from existing repositories still presents a significant challenge. The benchmark highlights the need for further advancements in LLM-based agents, particularly in areas such as repository reasoning and code editing. Future research efforts focused on these areas will be crucial for developing agents capable of reliably reproducing research results.
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