The May 17, 2023 academic paper explores the nature of **in-context learning (ICL)** in neural sequence models, particularly transformers, by investigating whether they implicitly implement **standard learning algorithms** like linear regression without parameter updates. Theoretically, the authors demonstrate that transformers can be constructed to implement algorithms such as **gradient descent** and **closed-form ridge regression** with limited computational capacity. Empirically, they show that the behavior of trained ICL models closely aligns with **minimum-Bayes-risk predictors**, transitioning between different algorithms like **ordinary least squares (OLS)** and ridge regression as model depth and dataset noise vary. Furthermore, using probing techniques, the research finds that ICL models encode meaningful intermediate quantities, suggesting that this phenomenon is algorithmically understandable and that transformers may **rediscover established estimation algorithms**.

Source:

https://arxiv.org/pdf/2211.15661