Summary of Kam-cot: Knowledge Augmented Multimodal Chain-of-thoughts Reasoning, by Debjyoti Mondal et al.
KAM-CoT: Knowledge Augmented Multimodal Chain-of-Thoughts Reasoning
by Debjyoti Mondal, Suraj Modi, Subhadarshi Panda, Rituraj Singh, Godawari Sudhakar Rao
First submitted to arxiv on: 23 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a framework called KAM-CoT that integrates chain of thought reasoning, knowledge graphs, and multiple modalities to achieve comprehensive understanding in multimodal tasks. The framework adopts a two-stage training process with knowledge graph grounding to generate effective rationales and answers. By incorporating external knowledge from knowledge graphs during reasoning, the model gains a deeper contextual understanding, reducing hallucinations and enhancing the quality of answers. This approach empowers the model to handle questions requiring external context, providing more informed answers. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way for computers to understand things by combining different types of information. It’s like a superpower that helps computers answer tricky questions correctly. The idea is based on how humans think step-by-step, and it uses special kinds of diagrams called knowledge graphs to help the computer remember important facts. This approach works really well and can even beat other powerful computer systems at answering questions. |
Keywords
* Artificial intelligence * Grounding * Knowledge graph
