Summary of Llm Processes: Numerical Predictive Distributions Conditioned on Natural Language, by James Requeima et al.
LLM Processes: Numerical Predictive Distributions Conditioned on Natural Language
by James Requeima, John Bronskill, Dami Choi, Richard E. Turner, David Duvenaud
First submitted to arxiv on: 21 May 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Computation and Language (cs.CL); 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 Machine learning practitioners often struggle to integrate their prior knowledge and beliefs into predictive models, limiting nuanced and context-aware analyses. Our goal is to build a regression model that processes numerical data and makes probabilistic predictions guided by natural language text describing users’ prior knowledge. We leverage Large Language Models (LLMs) as a starting point, as they provide an interface for incorporating expert insights in natural language and leveraging latent problem-relevant knowledge encoded in LLMs that users may not have themselves. We explore strategies for eliciting explicit, coherent numerical predictive distributions from LLMs, examining joint predictive distributions called LLM Processes over arbitrarily-many quantities in settings such as forecasting, multi-dimensional regression, black-box optimization, and image modeling. We investigate prompting details to elicit coherent predictive distributions and demonstrate their effectiveness at regression. Finally, we show the ability to incorporate text into numerical predictions, improving performance and giving quantitative structure that reflects qualitative descriptions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine learning experts have trouble using their prior knowledge to make accurate predictions. To solve this problem, we want to create a tool that takes in natural language text about someone’s prior knowledge and uses it to make probabilistic predictions about numerical data. We use Large Language Models (LLMs) as the starting point because they can understand natural language and have hidden knowledge that users may not know themselves. Our goal is to make this process easier by creating a way to get accurate, detailed predictions from LLMs. |
Keywords
» Artificial intelligence » Machine learning » Optimization » Prompting » Regression
