  When answering a question, humans utilize the information available across
different modalities to synthesize a consistent and complete chain of thought
(CoT). This process is normally a black box in the case of deep learning models
like large-scale language models. Recently, science question benchmarks have
been used to diagnose the multi-hop reasoning ability and interpretability of
an AI system. However, existing datasets fail to provide annotations for the
answers, or are restricted to the textual-only modality, small scales, and
limited domain diversity. To this end, we present Science Question Answering
(ScienceQA), a new benchmark that consists of ~21k multimodal multiple choice
questions with a diverse set of science topics and annotations of their answers
with corresponding lectures and explanations. We further design language models
to learn to generate lectures and explanations as the chain of thought (CoT) to
mimic the multi-hop reasoning process when answering ScienceQA questions.
ScienceQA demonstrates the utility of CoT in language models, as CoT improves
the question answering performance by 1.20% in few-shot GPT-3 and 3.99% in
fine-tuned UnifiedQA. We also explore the upper bound for models to leverage
explanations by feeding those in the input; we observe that it improves the
few-shot performance of GPT-3 by 18.96%. Our analysis further shows that
language models, similar to humans, benefit from explanations to learn from
fewer data and achieve the same performance with just 40% of the data. The data
and code are available at https://scienceqa.github.io.
