Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates.
The Learnability of In-Context Learning
Authors: Noam Wies, Yoav Levine, Amnon Shashua
NeurIPS 2023 | Venue PDF | LLM Run Details | Input Tokens: 20,221 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,068 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we propose a first-of-its-kind PAC based framework for in-context learnability, and use it to provide the first finite sample complexity results for the in-context learning setup. Our framework includes an initial pretraining phase, which fits a function to the pretraining distribution, and then a second in-context learning phase, which keeps this function constant and concatenates training examples of the downstream task in its input. Our theoretical analysis reveals that in this setting, in-context learning is more about identifying the task than about learning it, a result which is in line with a series of recent empirical findings. |
| Researcher Affiliation | Academia | Noam Wies, Yoav Levine & Amnon Shashua The Hebrew University of Jerusalem EMAIL |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper is theoretical and does not describe an implemented methodology for which source code would be provided. |
| Open Datasets | No | The paper is theoretical and does not use concrete, publicly available datasets for experiments. It discusses abstract “pretraining distributions” and “downstream task distributions”. |
| Dataset Splits | No | The paper is theoretical and does not report on experiments with dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not describe experimental procedures that would require specific hardware. No hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not describe experimental procedures that would require specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with specific hyperparameters or system-level training settings. |