Summary of Data-driven Discovery with Large Generative Models, by Bodhisattwa Prasad Majumder et al.
Data-driven Discovery with Large Generative Models
by Bodhisattwa Prasad Majumder, Harshit Surana, Dhruv Agarwal, Sanchaita Hazra, Ashish Sabharwal, Peter Clark
First submitted to arxiv on: 21 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This position paper calls upon the Machine Learning (ML) community to harness the potential of large generative models (LGMs) in developing automated systems for end-to-end data-driven discovery. The authors outline several key requirements for such a system, including scalability, reliability, and robustness. Through their proof-of-concept, DATAVOYAGER, utilizing GPT-4, they demonstrate how LGMs can fulfill some of these requirements, while also highlighting limitations that present opportunities for novel ML research. The paper argues that achieving accurate, reliable, and robust end-to-end discovery systems solely through the current capabilities of LGMs is challenging, instead advocating for fail-proof tool integration and active user moderation through feedback mechanisms to foster data-driven scientific discoveries with efficiency and reproducibility. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large generative models (LGMs) can help scientists make new discoveries by automatically searching through huge amounts of data. This paper talks about how LGMs can be used to develop systems that find answers all on their own, without needing any extra information or experiments. The authors think that these systems should be able to handle lots of data, be reliable and accurate, and work well even when things get tricky. They show a special tool called DATAVOYAGER that uses GPT-4 to do this, but they also say it’s not perfect yet. Instead, they suggest combining LGMs with other tools and letting people help correct any mistakes to make discoveries faster and more reliable. |
Keywords
* Artificial intelligence * Gpt * Machine learning
