Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Data-driven Discovery with Large Generative Models

Bodhisattwa Prasad Majumder, Harshit Surana, Dhruv Agarwal, Sanchaita Hazra, Ashish Sabharwal, Peter Clark

TL;DR

The paper investigates automating end-to-end data-driven scientific discovery using large generative models (LGMs) and presents DataVoyager, a GPT-4–based multi-agent prototype that performs data understanding, hypothesis generation, planning, and hypothesis verification on provided datasets. It demonstrates that LGMs can handle several discovery stages but are not yet sufficient for fully autonomous, reliable end-to-end discovery without robust tool integration and human-in-the-loop supervision. The authors argue for a practical pathway that combines LGM capabilities with fail-proof tools, continual learning, and user moderation to ensure efficiency, reproducibility, and safety. This work highlights both the promise of LGMs in accelerating discovery and the substantial research agenda needed to address limitations, risks, and ethical considerations.

Abstract

With the accumulation of data at an unprecedented rate, its potential to fuel scientific discovery is growing exponentially. This position paper urges the Machine Learning (ML) community to exploit the capabilities of large generative models (LGMs) to develop automated systems for end-to-end data-driven discovery -- a paradigm encompassing the search and verification of hypotheses purely from a set of provided datasets, without the need for additional data collection or physical experiments. We first outline several desiderata for an ideal data-driven discovery system. Then, through DATAVOYAGER, a proof-of-concept utilizing GPT-4, we demonstrate how LGMs fulfill several of these desiderata -- a feat previously unattainable -- while also highlighting important limitations in the current system that open up opportunities for novel ML research. We contend that achieving accurate, reliable, and robust end-to-end discovery systems solely through the current capabilities of LGMs is challenging. We instead advocate for fail-proof tool integration, along with active user moderation through feedback mechanisms, to foster data-driven scientific discoveries with efficiency and reproducibility.

Data-driven Discovery with Large Generative Models

TL;DR

The paper investigates automating end-to-end data-driven scientific discovery using large generative models (LGMs) and presents DataVoyager, a GPT-4–based multi-agent prototype that performs data understanding, hypothesis generation, planning, and hypothesis verification on provided datasets. It demonstrates that LGMs can handle several discovery stages but are not yet sufficient for fully autonomous, reliable end-to-end discovery without robust tool integration and human-in-the-loop supervision. The authors argue for a practical pathway that combines LGM capabilities with fail-proof tools, continual learning, and user moderation to ensure efficiency, reproducibility, and safety. This work highlights both the promise of LGMs in accelerating discovery and the substantial research agenda needed to address limitations, risks, and ethical considerations.

Abstract

With the accumulation of data at an unprecedented rate, its potential to fuel scientific discovery is growing exponentially. This position paper urges the Machine Learning (ML) community to exploit the capabilities of large generative models (LGMs) to develop automated systems for end-to-end data-driven discovery -- a paradigm encompassing the search and verification of hypotheses purely from a set of provided datasets, without the need for additional data collection or physical experiments. We first outline several desiderata for an ideal data-driven discovery system. Then, through DATAVOYAGER, a proof-of-concept utilizing GPT-4, we demonstrate how LGMs fulfill several of these desiderata -- a feat previously unattainable -- while also highlighting important limitations in the current system that open up opportunities for novel ML research. We contend that achieving accurate, reliable, and robust end-to-end discovery systems solely through the current capabilities of LGMs is challenging. We instead advocate for fail-proof tool integration, along with active user moderation through feedback mechanisms, to foster data-driven scientific discoveries with efficiency and reproducibility.
Paper Structure (14 sections, 20 figures)

This paper contains 14 sections, 20 figures.

Table of Contents

  1. Introduction
  2. DataVoyager: A Proof of Concept
  3. Towards Data-driven Discovery Systems
  4. Comprehensive Data Understanding
  5. Hypothesis Generation
  6. Planning and Orchestrating Research Pathways
  7. Hypothesis Evaluation
  8. Measurement of Progress
  9. Knowledge Integration
  10. Research Ethics and Fairness
  11. Limitations of Data-driven Discovery
  12. Survey on Related Systems
  13. Conclusion
  14. Impact Statement

Figures (20)

  • Figure 1: A blueprint flow demonstrating ideal workflows for data-driven discovery. Left: User asks an explicit question around a particular line of inquiry or hypothesis. Middle: The user can also ask a broad and partially-defined high-level question, where the system must figure out the appropriate datasets, data transformations, variables, a list of possible hypotheses, and their verification. In this example, the system maps time preference and health outcomes to exact variables, runs the analysis across appropriate demographic cuts, and then shares the significant findings for further exploration and verification. Right: The user can provide follow-up feedback at any time and the continual learner will learn from it while providing updated experiments and results.
  • Figure 2: An example workflow of DataVoyager. Starting from a user-provided dataset and a high-level query, it navigates through cycles of hypothesis generation, validation, and analysis to uncover complex insights. See all examples in Appendix for full understanding.
  • Figure 3: Survey across several dimensions of a proposed data discovery system for several existing automated and semi-automated data analysis and discovery systems such as: MLAgentBench Huang2023BenchmarkingLL, CoScientist Boiko2023AutonomousCR, Bacon Langley1977BACONAP, DataLume Gu2023HowDD, ThoughtSpot (thoughtspot.com), Google AutoML (cloud.google.com/automl), and Automatic Analysis* from WolframAlpha (wolframalpha.com/examples/pro-features/data-input).
  • Figure 4: Agent Structure for DataVoyager. Group Agent Chat has AutoGen agents that communicate with each other. The User Proxy links the user with the agents to share data, feedback, and goals. Code Execution Environment has access to structured functions and code generation methods that can be called depending on the context.
  • Figure 5: Data Analysis Tool Bench that can be structured inside DataVoyager to enable discovery in a wide range of scientific domains.
  • ...and 15 more figures