Notice: 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. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
In-Context Language Learning: Architectures and Algorithms
Authors: Ekin Akyürek, Bailin Wang, Yoon Kim, Jacob Andreas
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate a diverse set of neural sequence models on regular ICLL tasks. We first show that Transformers significantly outperform neural sequence models with recurrent or convolutional representations on ICLL tasks. |
| Researcher Affiliation | Academia | Ekin Aky urek 1 Bailin Wang 1 Yoon Kim 1 Jacob Andreas 1 1MIT CSAIL. Correspondence to: Ekin Aky urek <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 In-context n-gram language model with back-off |
| Open Source Code | Yes | 1Code & data are released at github.com/berlino/seq icl |
| Open Datasets | Yes | URL https://huggingface.co/ datasets/cerebras/Slim Pajama-627B. |
| Dataset Splits | No | Finally, divide this collection of instances into training and test sets. We perform exhaustive search over the grid of hyper-parameters in Table 3 and pick the best setting best on validation set on ICLL and AR seperately. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory, or cloud instance types) were provided for the experiments. |
| Software Dependencies | No | No specific software dependencies with version numbers (e.g., 'Python 3.8, PyTorch 1.9') are explicitly mentioned in the paper's main text or appendices. |
| Experiment Setup | Yes | Table 3. Hyper-parameter search space for neural models. hidden size [64, 128, 256, 512, 1024] number of layers [1, 2, 4, 8, 12] number of heads [1, 2, 4] epochs [200, 400] batch size 32 optimizer [Adam W] learning rate [1e-4, 2.5e-4 ] weight decay [0.01, 0.1] βs [(0.9, 0.99)] scheduler Cosine Scheduler with Warmup minimum learning rate 2.5e-5 warm-up start learning rate 1e-7 warm-up steps 25000. We perform exhaustive search over the grid of hyper-parameters in Table 3 and pick the best setting best on validation set on ICLL and AR seperately. |