BBH
Harbor adapterBIG-Bench Hard evaluates advanced reasoning capabilities across 23 challenging tasks using over 6,500 English samples.
- Samples
- 6,511
- Released
- 2022
- Scores
- 59
Science Question Answering
ScienceQA — AI evaluation benchmark listed by opencompass.
$ npm i -g lettokenburn-cli$ ltb bench pull scienceqa
External harness. pip install opencompass && opencompass --models <model_cfg> --datasets ScienceQA_llmjudge_gen (OpenCompass dataset config dir 'ScienceQA') https://github.com/open-compass/opencompass
Runs happen on your machine with your own subscription; only the score summary is pushed. CLI docs
Every number links to where it was published. Low-confidence rows are muted and need per-cell verification.
Science Question Answering (ScienceQA) is a new benchmark that consists of 21,208 multimodal multiple choice questions with diverse science topics and annotations of their answers with corresponding lectures and explanations. Out of the questions in ScienceQA, 10,332 (48.7%) have an image context, 10,220 (48.2%) have a text context, and 6,532 (30.8%) have both. Most questions are annotated with grounded lectures (83.9%) and detailed explanations (90.5%). The lecture and explanation provide general external knowledge and specific reasons, respectively, for arriving at the correct answer. To the best of our knowledge, ScienceQA is the first large-scale multimodal dataset that annotates lectures and explanations for the answers.
ScienceQA, in contrast to previous datasets, has richer domain diversity from three subjects: natural science, language science, and social science. Questions in each subject are categorized first by the topic (Biology, Physics, Chemistry, etc.), then by the category (Plants, Cells, Animals, etc.), and finally by the skill (Classify fruits and vegetables as plant parts, Identify countries of Africa, etc.). ScienceQA features 26 topics, 127 categories, and 379 skills that cover a wide range of domains.