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Multimodal Chain-of-Thought Reasoning in Language Models | Papers With Code
Source: paperswithcode.com
Article summary:
1. This article proposes a two-stage framework called Multimodal-CoT that combines language (text) and vision (images) modalities to generate intermediate reasoning chains as the rationale to infer the answer.
2. The model outperforms the previous state-of-the-art LLM (GPT-3.5) by 16 percentage points on the ScienceQA benchmark and even surpasses human performance.
3. Code is publicly available at https://github.com/amazon-science/mm-cot
Article analysis:
The article is reliable and trustworthy, as it provides evidence for its claims in the form of results from experiments conducted on the ScienceQA benchmark, which shows that Multimodal CoT outperforms GPT-3.5 by 16 percentage points and even surpasses human performance. The code is also publicly available, which allows readers to verify the results for themselves if they wish to do so.
The article does not appear to be biased or one-sided, as it presents both sides of the argument equally and objectively without any promotional content or partiality towards either side. It also does not appear to have any unsupported claims or missing points of consideration, as all claims are backed up with evidence from experiments conducted on the ScienceQA benchmark.
The only potential issue with this article is that it does not explore any counterarguments or present any risks associated with using Multimodal CoT, such as potential privacy concerns due to collecting data from multiple sources or potential security issues due to combining multiple modalities into a single system. However, these issues are beyond the scope of this article and would require further research in order to be addressed properly.