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T-SciQ: Teaching Multimodal Chain-of-Thought Reasoning via Large Language Model Signals for Science Question Answering. (arXiv:2305.03453v1 [cs.CL])
cs.CL updates on arXiv.org arxiv.org
Large Language Models (LLMs) have recently demonstrated exceptional
performance in various Natural Language Processing (NLP) tasks. They have also
shown the ability to perform chain-of-thought (CoT) reasoning to solve complex
problems. Recent studies have explored CoT reasoning in complex multimodal
scenarios, such as the science question answering task, by fine-tuning
multimodal models with high-quality human-annotated CoT rationales. However,
collecting high-quality COT rationales is usually time-consuming and costly.
Besides, the annotated rationales are hardly accurate due to the redundant
information involved …
arxiv language language model language models language processing large language model large language models llms multimodal natural natural language natural language processing nlp performance processing question answering reasoning science studies teaching thought