Verified86%Harbor: pendingKnowledge & ReasoningMultimodal

ScienceQA

Science Question Answering

ScienceQA — AI evaluation benchmark listed by opencompass.

How to run

ltb · scienceqa
$ 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

Scores

83 sourced entries
HarnessSourceConfidenceLink
Mutimodal-T-SciQ_Large 🥇96.2N/AIndependentmediumN/Ascienceqa.github.io
MC-CoT_F-Large 🥈94.9N/AIndependentmediumN/Ascienceqa.github.io
VisionLLM v2-Chat94.4N/ASelf-reportedmediumN/Aproceedings.neurips.cc
Honeybee 🥉94.4N/AIndependentmediumN/Ascienceqa.github.io
Enigma-COT_Large94.1N/AIndependentmediumN/Ascienceqa.github.io
MC-CoT_Large93.4N/AIndependentmediumN/Ascienceqa.github.io
DPMM-CoT_Large93.3N/AIndependentmediumN/Ascienceqa.github.io
LLaVA92.5N/AIndependentmediumN/Ascienceqa.github.io
CoMD91.9N/AIndependentmediumN/Ascienceqa.github.io
Mutimodal-T-SciQ_Base91.8N/AIndependentmediumN/Ascienceqa.github.io
Multimodal-CoT_Large91.7N/AIndependentmediumN/Ascienceqa.github.io
Phi-3.5-Vision91.3N/ASelf-reportedmediumN/Aarxiv.org
PILL91.2N/AIndependentmediumN/Ascienceqa.github.io
DPMM-CoT_Base91.0N/AIndependentmediumN/Ascienceqa.github.io
LaVIN-13B90.8N/AIndependentmediumN/Ascienceqa.github.io
MC-CoT_F-Base90.7N/AIndependentmediumN/Ascienceqa.github.io
MC-CoT_Base90.6N/AIndependentmediumN/Ascienceqa.github.io
LLaMA-SciTune90.0N/AIndependentmediumN/Ascienceqa.github.io
LaVIN-7B89.4N/AIndependentmediumN/Ascienceqa.github.io
Flan-T5-XL89.3N/AIndependentmediumN/Ascienceqa.github.io
Chat-UniVi88.8N/AIndependentmediumN/Ascienceqa.github.io
GPT-4o88.5N/AIndependentmediumN/Aarxiv.org
DDCoT87.3N/AIndependentmediumN/Ascienceqa.github.io
LG-VQA87.2N/AIndependentmediumN/Ascienceqa.github.io
Chameleon86.5N/AIndependentmediumN/Ascienceqa.github.io
Enigma-COT_Base85.6N/AIndependentmediumN/Ascienceqa.github.io
LLaMA-Adapter85.2N/AIndependentmediumN/Ascienceqa.github.io
Multimodal-CoT_Base84.9N/AIndependentmediumN/Ascienceqa.github.io
IMMO SL+RL84.8N/AIndependentmediumN/Ascienceqa.github.io
HoT-T5_Large83.4N/AIndependentmediumN/Ascienceqa.github.io
HoT-T5_Base81.4N/AIndependentmediumN/Ascienceqa.github.io
Gemini 1.0 Pro Vision79.7N/AIndependentmediumN/Aarxiv.org
UnifiedQA-T-SciQ_Base79.4N/AIndependentmediumN/Ascienceqa.github.io
falcon-11B-vlm74.9N/ASelf-reportedmediumN/Aarxiv.org
BLIP-2 OPT 2.7B74.2N/AIndependentmediumN/Ascienceqa.github.io
text-davinci-00174.0N/AIndependentmediumN/Ascienceqa.github.io
Llama3 LLaVA Next 8B73.7N/AIndependentmediumN/Aarxiv.org
LLaVA-NeXT-13B73.6N/AIndependentmediumN/Aproceedings.neurips.cc
LLaVA V1.6 Vicuna 13B73.6N/AIndependentmediumN/Aarxiv.org
InstructBLIP73.3N/AIndependentmediumN/Ascienceqa.github.io
LLaVA V1.6 Mistral 7B72.8N/AIndependentmediumN/Aarxiv.org
MM1-7B-Chat72.6N/AIndependentmediumN/Aarxiv.org
Claude 3 Haiku72.0N/AIndependentmediumN/Aarxiv.org
LLaVA V1.6 Vicuna 7B70.6N/AIndependentmediumN/Aarxiv.org
UnifiedQA70.1N/AIndependentmediumN/Ascienceqa.github.io
InternVL-Chat-ViT-6B-Vicuna-13B70.1N/AIndependentmediumN/Aproceedings.neurips.cc
LLaVA-NeXT-7B70.1N/AIndependentmediumN/Aproceedings.neurips.cc
ChatGPT69.4N/AIndependentmediumN/Ascienceqa.github.io
MM1-3B-Chat69.4N/AIndependentmediumN/Aarxiv.org
MetaCLIP68.8N/AIndependentmediumN/Ascienceqa.github.io
Top 50 of 83 by score

Every number links to where it was published. Low-confidence rows are muted and need per-cell verification.

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About

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.