Summary of Autoelicit: Using Large Language Models For Expert Prior Elicitation in Predictive Modelling, by Alexander Capstick et al.
AutoElicit: Using Large Language Models for Expert Prior Elicitation in Predictive Modelling
by Alexander Capstick, Rahul G. Krishnan, Payam Barnaghi
First submitted to arxiv on: 26 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL); Machine Learning (stat.ML)
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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 The proposed paper introduces AutoElicit, a method to extract knowledge from large language models (LLMs) and construct priors for predictive models. The approach is designed for domains where labelled data is scarce or expensive, such as healthcare, biology, and finance. By using Bayesian inference with well-specified prior distributions over model parameters, the sample complexity of learning can be reduced. The paper compares AutoElicit with in-context learning and demonstrates how to perform model selection between the two methods. The results show that AutoElicit yields priors that can substantially reduce error over uninformative priors, using fewer labels, and consistently outperform in-context learning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AutoElicit is a new way to use big language models to help build better predictive models for important tasks like diagnosing medical conditions or predicting financial trends. These models are really good at understanding lots of information, but they can be slow and hard to understand. AutoElicit takes the knowledge from these big models and uses it to make predictions that are more accurate and easier to understand. This is especially helpful in fields where there isn’t much labeled data available or when experts need help creating better predictive models. |
Keywords
» Artificial intelligence » Bayesian inference
