datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
PatchBench
PatchBench
PatchBench is a benchmark for evaluating AI agents on realistic vulnerability patching tasks: 213 tasks drawn from 32 popular GitHub C/C++ projects. It selects vulnerabilities whose ground-truth fixes lie outside the crash stack, and uses vulnerability transplant plus code mutation to mitigate surface-level fixes and patch memorization.
This repository holds the task metadata, one row per task to identify the project, the exact repository state, the crash, and the… See the full description on the dataset page: https://huggingface.co/datasets/ai-sec-lab/PatchBench.Patchnoisseur
Patchnoisseur
A connoisseur's cellar of CVEs: every NVD CVE joined to its fixing-commit
diff (when one could be found), its NVD description, and its
associated CWE(s) (id, name, short description) — served as a single
Parquet dataset.
351 884 CVEs · 25 015 with a real git diff attached · CVE-1999 → CVE-2026
· ~744 MB on disk (zstd-compressed Parquet, sharded ~300 MB each).
What's in it
One row per CVE in the NVD feed. CVEs without a retrievable patch are… See the full description on the dataset page: https://huggingface.co/datasets/michoo42/Patchnoisseur.act_patch_llama_3.1_8b_counterfact
Training Language Models to Explain Their Own Computations
Paper | Code
This dataset contains activation patching results used for training explainer models to predict how internal interventions affect target model outputs. It was introduced in the paper "Training Language Models to Explain Their Own Computations".
Dataset Summary
The dataset covers the Activation Patching task for the Llama-3.1-8B target model, where explainer models learn to predict the effects of… See the full description on the dataset page: https://huggingface.co/datasets/Transluce/act_patch_llama_3.1_8b_counterfact.act_patch_qwen3_8b_counterfact
Training Language Models to Explain Their Own Computations
This dataset is part of the work presented in the paper "Training Language Models To Explain Their Own Computations".
Code | Paper
Introduction
Language models (LMs) can learn to faithfully describe their internal computations. This dataset contains results from Activation Patching interventions on the Qwen3-8B target model using CounterFact data. Explainer models are trained on this data to predict how… See the full description on the dataset page: https://huggingface.co/datasets/Transluce/act_patch_qwen3_8b_counterfact.PatchBP