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Towards Data-Centric Automatic R&D

Haotian Chen, Xinjie Shen, Zeqi Ye, Wenjun Feng, Haoxue Wang, Xiao Yang, Xu Yang, Weiqing Liu, Jiang Bian

TL;DR

This work tackles the challenge of automating data-centric scientific R&D by introducing RD2Bench, a real-world benchmark that evaluates end-to-end data-centric automatic R&D (D-CARD) and the interaction among model capabilities. RD2Bench covers data collection, method extraction, and method implementation, with metrics designed to favor trustworthy, reproducible results. Experiments reveal that while GPT-4 variants demonstrate substantial potential, there are clear limitations in domain knowledge handling and scalability to harder formulations. The benchmark provides a foundation for developing techniques and models capable of autonomous R&D, with potential to significantly improve research efficiency and productivity in data-driven domains.

Abstract

The progress of humanity is driven by those successful discoveries accompanied by countless failed experiments. Researchers often seek the potential research directions by reading and then verifying them through experiments. The process imposes a significant burden on researchers. In the past decade, the data-driven black-box deep learning method has demonstrated its effectiveness in a wide range of real-world scenarios, which exacerbates the experimental burden of researchers and thus renders the potential successful discoveries veiled. Therefore, automating such a research and development (R&D) process is an urgent need. In this paper, we serve as the first effort to formalize the goal by proposing a Real-world Data-centric automatic R&D Benchmark, namely RD2Bench. RD2Bench benchmarks all the operations in data-centric automatic R&D (D-CARD) as a whole to navigate future work toward our goal directly. We focus on evaluating the interaction and synergistic effects of various model capabilities and aiding in selecting well-performing trustworthy models. Although RD2Bench is very challenging to the state-of-the-art (SOTA) large language model (LLM) named GPT-4, indicating ample research opportunities and more research efforts, LLMs possess promising potential to bring more significant development to D-CARD: They are able to implement some simple methods without adopting any additional techniques. We appeal to future work to take developing techniques for tackling automatic R&D into consideration, thus bringing the opportunities of the potential revolutionary upgrade to human productivity.

Towards Data-Centric Automatic R&D

TL;DR

This work tackles the challenge of automating data-centric scientific R&D by introducing RD2Bench, a real-world benchmark that evaluates end-to-end data-centric automatic R&D (D-CARD) and the interaction among model capabilities. RD2Bench covers data collection, method extraction, and method implementation, with metrics designed to favor trustworthy, reproducible results. Experiments reveal that while GPT-4 variants demonstrate substantial potential, there are clear limitations in domain knowledge handling and scalability to harder formulations. The benchmark provides a foundation for developing techniques and models capable of autonomous R&D, with potential to significantly improve research efficiency and productivity in data-driven domains.

Abstract

The progress of humanity is driven by those successful discoveries accompanied by countless failed experiments. Researchers often seek the potential research directions by reading and then verifying them through experiments. The process imposes a significant burden on researchers. In the past decade, the data-driven black-box deep learning method has demonstrated its effectiveness in a wide range of real-world scenarios, which exacerbates the experimental burden of researchers and thus renders the potential successful discoveries veiled. Therefore, automating such a research and development (R&D) process is an urgent need. In this paper, we serve as the first effort to formalize the goal by proposing a Real-world Data-centric automatic R&D Benchmark, namely RD2Bench. RD2Bench benchmarks all the operations in data-centric automatic R&D (D-CARD) as a whole to navigate future work toward our goal directly. We focus on evaluating the interaction and synergistic effects of various model capabilities and aiding in selecting well-performing trustworthy models. Although RD2Bench is very challenging to the state-of-the-art (SOTA) large language model (LLM) named GPT-4, indicating ample research opportunities and more research efforts, LLMs possess promising potential to bring more significant development to D-CARD: They are able to implement some simple methods without adopting any additional techniques. We appeal to future work to take developing techniques for tackling automatic R&D into consideration, thus bringing the opportunities of the potential revolutionary upgrade to human productivity.
Paper Structure (19 sections, 4 equations, 3 figures, 10 tables)

This paper contains 19 sections, 4 equations, 3 figures, 10 tables.

Table of Contents

  1. Introduction
  2. RD2Bench
  3. Data Collection
  4. Method Extraction
  5. Method Implementation
  6. Metrics
  7. Experiments
  8. Experimental Settings
  9. Results of Formula Implementation
  10. Results of Model Architecture Implementation
  11. Limitations
  12. Relate Work
  13. LLM as Autonomous Agent
  14. Semi-Automatic R&D with Agents
  15. Conclusion
  16. ...and 4 more sections

Figures (3)

  • Figure 1: An overview of the R&D process. Researchers read papers and reports to extract the implementable methods (usually formulated as mathematical formulas or model architectures) for seeking potential research directions. Then, they correctly implement the methods to obtain the results for further analysis and development.
  • Figure 2: An example of formula implementation task.
  • Figure 3: An example of metrics calculation for model architecture implementation task.