{"url":"/dataset/ginc","name":"GINC","full_name":"Generative IN-Context learning Dataset","description_markdown":"GINC (Generative In-Context learning Dataset) is a small-scale synthetic dataset for studying in-context learning. The pretraining data is generated by a mixture of HMMs and the in-context learning prompt examples are also generated from HMMs (either from the mixture or not). The prompt examples are out-of-distribution with respect to the pretraining data since every example is independent, concatenated, and separated by delimiters. The GitHub repository provides code to generate GINC-style datasets of varying vocabulary sizes, number of HMMs, and other parameters.","description_withheld":null,"homepage":"https://github.com/p-lambda/incontext-learning","introduced_date":"2021-11-03","introduced_date_note":null,"introduced_by":{"paper":"/paper/an-explanation-of-in-context-learning-as-1","title":"An Explanation of In-context Learning as Implicit Bayesian Inference","first_author":"Sang Michael Xie","url":null},"license":null,"modalities":[{"name":"Texts","url":"/datasets/modality/texts"}],"tasks":[{"name":"Few-Shot Learning","url":"/task/few-shot-learning","datasets_with_task":"/datasets/task/few-shot-learning"},{"name":"Language Modelling","url":"/task/language-modelling","datasets_with_task":"/datasets/task/language-modelling"}],"languages":[],"variants":["GINC"],"data_loaders":[{"repo":"https://github.com/p-lambda/incontext-learning","url":"https://github.com/p-lambda/incontext-learning","frameworks":[]}],"num_papers_in_archive":7,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}