MetaGraph uses ontology-guided LLM extraction to build knowledge graphs from 681 papers on GenAI in financial NLP, identifying three distinct phases of development from 2022 to 2025.
ISBN 9798400710810
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Prompting and agent methods boost standard LLMs on financial QA by simulating long chain-of-thought reasoning, but reasoning LLMs already have this capability and show limited further gains, while multilingual alignment helps mainly by lengthening reasoning with minimal benefit for reasoning models.
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MetaGraph: A Large-Scale Meta-Analysis of GenAI in Financial NLP (2022-2025)
MetaGraph uses ontology-guided LLM extraction to build knowledge graphs from 681 papers on GenAI in financial NLP, identifying three distinct phases of development from 2022 to 2025.
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What Factors Affect LLMs and RLLMs in Financial Question Answering?
Prompting and agent methods boost standard LLMs on financial QA by simulating long chain-of-thought reasoning, but reasoning LLMs already have this capability and show limited further gains, while multilingual alignment helps mainly by lengthening reasoning with minimal benefit for reasoning models.