{"url":"/dataset/repoqa","name":"RepoQA","full_name":null,"description_markdown":"RepoQA is a benchmark that aims to exercise the long-context code understanding ability of Language Learning Models (LLMs)². It supports repositories from 5 programming languages: Python, C++, TypeScript, Rust, and Java². \r\n\r\nThe goal of RepoQA is to evaluate LLMs on long-context tasks that can reflect real-life uses². For example, it includes a function called `repoqa.search_needle_function` that evaluates model outputs and computes scores after text generation³. \r\n\r\n(1) Releases · evalplus/repoqa · GitHub. https://github.com/evalplus/repoqa/releases.\r\n(2) RepoQA: Evaluating Long-Context Code Understanding - GitHub. https://github.com/evalplus/repoqa.\r\n(3) BrowserBench.org — Browser Benchmarks. https://browserbench.org/.","description_withheld":null,"homepage":"https://evalplus.github.io/repoqa.html","introduced_date":null,"introduced_date_note":null,"introduced_by":null,"license":null,"modalities":[],"tasks":[],"languages":[],"variants":["RepoQA"],"data_loaders":[],"num_papers_in_archive":0,"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."}