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Structure First, Reason Next: Enhancing a Large Language Model using Knowledge Graph for Numerical Reasoning in Financial Documents
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This framework proposes an end-to-end pipeline where LLMs are used to construct Knowledge Graphs (KGs) from financial documents (leveraging LLM's understanding for triplet generation) and then these KGs, after filtering, are used to enhance the LLM's numerical reasoning capabilities. This bidirectional and integrated approach, where LLM capabilities are used to create structured knowledge that subsequently guides and improves the LLM's reasoning, aligns with a synergized model for reasoning.
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