Summary of Can Generative Ai Solve Your In-context Learning Problem? a Martingale Perspective, by Andrew Jesson and Nicolas Beltran-velez and David Blei
Can Generative AI Solve Your In-Context Learning Problem? A Martingale Perspective
by Andrew Jesson, Nicolas Beltran-Velez, David Blei
First submitted to arxiv on: 8 Dec 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Artificial Intelligence (cs.AI); 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 This work explores when conditional generative models (CGMs) can solve in-context learning (ICL) problems. An ICL problem involves a CGM, dataset, and prediction task. The study proposes Bayesian model criticism as an approach to assess the suitability of a given CGM for an ICL problem. However, contemporary CGMs do not explicitly provide likelihood and posterior distributions, making it difficult to perform posterior predictive checks (PPCs). To address this challenge, the authors show that ancestral sampling from the predictive distribution of a CGM is equivalent to sampling datasets from the posterior predictive of the assumed Bayesian model. This leads to the development of the generative predictive p-value, which enables PPCs and similar methods for contemporary CGMs. The p-value can be used in a statistical decision procedure to determine when the model is suitable for an ICL problem. The method only requires generating queries and responses from a CGM and evaluating its response log probability. The authors empirically evaluate their method on synthetic tabular, imaging, and natural language ICL tasks using large language models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this study, researchers investigate when artificial intelligence models can learn new information without being explicitly trained for that task. They look at how these models, called conditional generative models (CGMs), perform in specific situations where they need to understand the context of a problem and provide a solution. The team proposes a way to evaluate if these CGMs are suitable for a particular task by analyzing their predictions and comparing them to actual results. This method can help us better understand when these AI models are trustworthy and reliable. |
Keywords
» Artificial intelligence » Likelihood » Probability
