MLAgentBench: Evaluating Language Agents on Machine Learning Experimentation (2310.03302v2)

Abstract: A central aspect of machine learning research is experimentation, the process of designing and running experiments, analyzing the results, and iterating towards some positive outcome (e.g., improving accuracy). Could agents driven by powerful LLMs perform machine learning experimentation effectively? To answer this question, we introduce MLAgentBench, a suite of 13 tasks ranging from improving model performance on CIFAR-10 to recent research problems like BabyLM. For each task, an agent can perform actions like reading/writing files, executing code, and inspecting outputs. We then construct an agent that can perform ML experimentation based on ReAct framework. We benchmark agents based on Claude v1.0, Claude v2.1, Claude v3 Opus, GPT-4, GPT-4-turbo, Gemini-Pro, and Mixtral and find that a Claude v3 Opus agent is the best in terms of success rate. It can build compelling ML models over many tasks in MLAgentBench with 37.5% average success rate. Our agents also display highly interpretable plans and actions. However, the success rates vary considerably; they span from 100% on well-established older datasets to as low as 0% on recent Kaggle challenges created potentially after the underlying LM was trained. Finally, we identify several key challenges for LM-based agents such as long-term planning and reducing hallucination. Our code is released at https://github.com/snap-stanford/MLAgentBench.

Insights on "Benchmarking LLMs as AI Research Agents"

Overview

The paper "Benchmarking LLMs as AI Research Agents" explores the potential of AI agents, specifically those leveraging LLMs, in conducting end-to-end ML research tasks. These tasks mimic the iterative processes carried out by human researchers, such as hypothesis formation, experimentation, and analysis.

MLAgentBench Framework

The authors introduce MLAgentBench, a novel suite designed to evaluate AI research agents on defined machine learning tasks. These tasks involve data processing, model architecture design, and training, allowing agents to execute complex scripts and interact with varied data types. The benchmark assesses agent performance based on metrics like success rate, improvement magnitude, reasoning quality, and efficiency.

Experimental Setup

A GPT-4-based research agent was designed to interact within the MLAgentBench environment, tasked with automatically performing research loops. The agent employs AI capabilities such as reading, editing code, and executing experiments, with actions logged in an interaction trace for comprehensive evaluation.

Empirical Findings

The research agent can indeed develop high-performing ML models, albeit with significant variability in success across tasks. For instance, the agent achieved near 90% success on established datasets like cifar10 but struggled with newer research challenges such as recent Kaggle competitions, highlighting challenges like long-term planning and hallucination in LLM-based research agents.

Key Challenges and Implications

The paper uncovers several challenges facing LLM-based research agents:

• Long-term Planning: Effective long-term strategy formulation remains a hurdle, impacting agent performance on more complex tasks.
• Hallucination Phenomenon: Incorrect conclusions or assumptions, termed 'hallucinations,' detract from reliability.
• Resource Efficiency: The costs associated with high computational resource usage remain a constraint against scalability.

Future Directions

The paper advocates further development of AI research agents and suggests broadening MLAgentBench's tasks to encompass a wider array of scientific domains, promoting the synergy between human and AI-driven research.

Conclusion

This paper provides a structured evaluation of AI research agents in machine learning, offering valuable insights into the feasibility of automated scientific exploration. The MLAgentBench serves as a crucial framework for assessing the capabilities and challenges of LLM-based generative agents, paving the way for innovations in AI-driven scientific inquiry.