Summary of Is In-context Learning in Large Language Models Bayesian? a Martingale Perspective, by Fabian Falck et al.
Is In-Context Learning in Large Language Models Bayesian? A Martingale Perspective
by Fabian Falck, Ziyu Wang, Chris Holmes
First submitted to arxiv on: 2 Jun 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
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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 paper investigates whether Large Language Models (LLMs) employ in-context learning (ICL), where they can make predictions without fine-tuning when given a pretrained model and an observed dataset. The authors analyze this phenomenon through the lens of the martingale property, a fundamental requirement for Bayesian inference on exchangeable data. They show that the martingale property is necessary for unambiguous predictions and enables a decomposed notion of uncertainty crucial in safety-critical systems. The paper provides actionable checks with theory and test statistics to verify if the martingale property holds. Additionally, it examines whether uncertainty decreases as expected in Bayesian learning when more data is observed. In three experiments, the authors find evidence for violations of the martingale property and deviations from a Bayesian scaling behaviour of uncertainty, refuting the hypothesis that ICL is approximately Bayesian. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper looks at how Large Language Models (LLMs) can make predictions without needing more training when given some data. It’s like trying to guess what will happen next in a story based on what has happened so far. The authors want to know if LLMs are really doing this, or if it just seems that way. They use a special idea called the martingale property to figure out how LLMs work. They find that sometimes LLMs don’t follow this pattern, which means their predictions might not be as good as we thought. |
Keywords
» Artificial intelligence » Bayesian inference » Fine tuning
