SciCoT: A Science and Medical Question-Answering Instruction Dataset

SciCoT is a text dataset for instruction tuning, combining up to 3,000 examples each from the SciQ, MedMCQA, and PubMed_QA (human-annotated) datasets. It was created by author 'euclaise' and last updated on Hugging Face in November 2023. The dataset's composition suggests a focus on scientific and medical multiple-choice and question-answering tasks.

Use Cases

• Fine-tuning language models for science question-answering based on the SciQ component.
• Training models on medical multiple-choice questions based on the MedMCQA component.
• Developing biomedical literature comprehension models based on the PubMed_QA component.
• Creating instruction-following benchmarks for evaluating model reasoning in technical domains.

Strengths

• Combines three established science and medical QA datasets, providing a multi-domain resource.
• Caps examples at 3,000 per source, which may help balance the contribution of each constituent dataset.
• Includes the human-annotated subset of PubMed_QA, which likely contains verified answers.

Limitations

• Description metadata is limited; actual data quality requires manual inspection after download.
• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.

Provenance

Source

Combination of SciQ, MedMCQA, and PubMed_QA datasets.

Collection Method

Curated combination with a maximum cap per source.

Freshness

Last updated 2023-11-21 19:31:01; freshness should be verified.