MLE-bench
LlamaCast18 Loka 2024

MLE-bench

🤖 MLE-bench: Evaluating Machine Learning Agents on Machine Learning Engineering

The paper introduces MLE-bench, a benchmark designed to evaluate AI agents' ability to perform machine learning engineering tasks. The benchmark comprises 75 Kaggle competitions, each requiring agents to solve real-world problems involving data preparation, model training, and code debugging. Researchers evaluated several cutting-edge language models on MLE-bench, with the best-performing setup achieving at least a bronze medal in 16.9% of the competitions. The paper investigates various factors influencing performance, such as resource scaling and contamination from pre-training, and concludes that while current agents demonstrate promising capabilities, significant challenges remain.

📎 Link to paper

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Jaksot(49)

Marco-o1

Marco-o1

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Scaling Laws for Precision

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Test-Time Training

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14 Marras 202414min

Qwen2.5-Coder

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12 Marras 202424min

Attacking Vision-Language Computer Agents via Pop-ups

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😈 Attacking Vision-Language Computer Agents via Pop-upsThis research paper examines vulnerabilities in vision-language models (VLMs) that power autonomous agents performing computer tasks. The author...

9 Marras 202421min

Number Cookbook

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📓 Number Cookbook: Number Understanding of Language Models and How to Improve ItThis research paper examines the numerical understanding and processing abilities (NUPA) of large language models (LLMs...

8 Marras 202416min

Jigsaw Puzzles

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🧩 Jigsaw Puzzles: Splitting Harmful Questions to Jailbreak Large Language ModelsThis research paper investigates the vulnerabilities of large language models (LLMs) to "jailbreak" attacks, where mali...

7 Marras 202416min

Multi-expert Prompting with LLMs

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🤝 Multi-expert Prompting with LLMsThe research paper presents Multi-expert Prompting, a novel method for improving the reliability, safety, and usefulness of Large Language Models (LLMs). Multi-exper...

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