Submitted by Singularian2501 t3_10svwch in MachineLearning
Paper: https://arxiv.org/abs/2302.00923
Github: https://github.com/amazon-science/mm-cot
Twitter: https://paperswithcode.com/top-social
Abstract:
>Large language models (LLMs) have shown impressive performance on complex reasoning by leveraging chain-of-thought (CoT) prompting to generate intermediate reasoning chains as the rationale to infer the answer. However, existing CoT studies are mostly isolated in the language modality with LLMs, where LLMs are hard to deploy. To elicit CoT reasoning in multimodality, a possible solution is to fine-tune small language models by fusing the vision and language features to perform CoT reasoning. The key challenge is that those language models tend to generate hallucinated reasoning chains that mislead the answer inference. To mitigate the effect of such mistakes, we propose Multimodal-CoT that incorporates vision features in a decoupled training framework. The framework separates the rationale generation and answer inference into two stages. By incorporating the vision features in both stages, the model is able to generate effective rationales that contribute to answer inference. With Multimodal-CoT, our model under 1 billion parameters outperforms the previous state-of-the-art LLM (GPT-3.5) by 16% (75.17%->91.68%) on the ScienceQA benchmark and even surpasses human performance.
throwaway2676 t1_j74iilz wrote
Imo, chain-of-thought and program-of-thought reasoning will be the next major generation of progress for LLMs. Probably another year or two and we will be able to eliminate those goofy instances where the models confidently produce nonsense (well, mostly anyway).