DARE-bench: Evaluating Modeling and Instruction Fidelity of LLMs in Data Science
arXiv:2602.24288v1 Announce Type: new Abstract: The fast-growing demands in using Large Language Models (LLMs) to tackle complex multi-step data science tasks create an emergent need for accurate benchmarking. There are two major gaps in existing benchmarks: (i) the lack of standardized, process-aware evaluation that captures instruction adherence and process fidelity, and (ii) the scarcity of accurately labeled training data. To bridge these gaps, we introduce DARE-bench, a benchmark designed for machine learning modeling and data science instruction following. Unlike many existing benchmarks that rely on human- or model-based judges, all tasks in DARE-bench have verifiable ground truth, ensuring objective and reproducible evaluation. To cover a broad range of tasks and support agentic tools, DARE-bench consists of 6,300 Kaggle-derived tasks and provides both large-scale training data and evaluation sets. Extensive evaluations show that even highly capable models such as gpt-o4-mini
arXiv:2602.24288v1 Announce Type: new Abstract: The fast-growing demands in using Large Language Models (LLMs) to tackle complex multi-step data science tasks create an emergent need for accurate benchmarking. There are two major gaps in existing benchmarks: (i) the lack of standardized, process-aware evaluation that captures instruction adherence and process fidelity, and (ii) the scarcity of accurately labeled training data. To bridge these gaps, we introduce DARE-bench, a benchmark designed for machine learning modeling and data science instruction following. Unlike many existing benchmarks that rely on human- or model-based judges, all tasks in DARE-bench have verifiable ground truth, ensuring objective and reproducible evaluation. To cover a broad range of tasks and support agentic tools, DARE-bench consists of 6,300 Kaggle-derived tasks and provides both large-scale training data and evaluation sets. Extensive evaluations show that even highly capable models such as gpt-o4-mini struggle to achieve good performance, especially in machine learning modeling tasks. Using DARE-bench training tasks for fine-tuning can substantially improve model performance. For example, supervised fine-tuning boosts Qwen3-32B's accuracy by 1.83x and reinforcement learning boosts Qwen3-4B's accuracy by more than 8x. These significant improvements verify the importance of DARE-bench both as an accurate evaluation benchmark and critical training data.
Executive Summary
The article presents DARE-bench, a novel benchmark designed to assess the modeling and instruction fidelity of Large Language Models (LLMs) in data science tasks. DARE-bench addresses two significant gaps in existing benchmarks: lack of standardized evaluation and scarcity of accurately labeled training data. With 6,300 Kaggle-derived tasks, DARE-bench provides both training and evaluation sets, ensuring objective and reproducible evaluation. The authors demonstrate the effectiveness of DARE-bench by fine-tuning various models, achieving substantial performance improvements. This breakthrough benchmark has the potential to revolutionize the development and evaluation of LLMs in data science. As the demand for complex multi-step data science tasks grows, DARE-bench fills a critical need in the field, offering a new standard for evaluating and training LLMs.
Key Points
- ▸ DARE-bench addresses two major gaps in existing benchmarks: lack of standardized evaluation and scarcity of accurately labeled training data.
- ▸ DARE-bench consists of 6,300 Kaggle-derived tasks, providing both training and evaluation sets.
- ▸ The authors demonstrate the effectiveness of DARE-bench by fine-tuning various models, achieving substantial performance improvements.
Merits
Strength in Standardization
DARE-bench provides a standardized evaluation framework, ensuring objective and reproducible assessment of LLMs.
Comprehensive Task Coverage
The inclusion of 6,300 Kaggle-derived tasks ensures a broad range of tasks and supports agentic tools.
Accurate Evaluation
All tasks in DARE-bench have verifiable ground truth, guaranteeing accurate evaluation of LLMs.
Demerits
Potential Overreliance on Kaggle Data
The use of Kaggle-derived tasks may lead to overfitting or biased model performance, particularly if the data is not representative of real-world scenarios.
Scalability and Maintenance
As the number of tasks and models increases, maintaining and updating DARE-bench may become challenging, requiring significant resources and effort.
Expert Commentary
The article's introduction of DARE-bench marks a significant advancement in the evaluation of Large Language Models (LLMs) in data science. The authors' demonstration of the effectiveness of DARE-bench in fine-tuning various models highlights the potential for substantial performance improvements. However, the potential overreliance on Kaggle data and scalability concerns require careful consideration. As the field of LLMs in data science continues to evolve, DARE-bench will play a critical role in shaping the development and evaluation of these models. The article's contributions have far-reaching implications for both researchers and practitioners, underscoring the need for standardized evaluation frameworks and accurately labeled training data.
Recommendations
- ✓ Develop and maintain a diverse set of tasks and models to ensure DARE-bench remains representative of real-world scenarios.
- ✓ Continuously update and refine DARE-bench to address emerging challenges and limitations in the development and evaluation of LLMs in data science.