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.
Pragya Srivastava, Manuj Malik, Vivek Gupta, Tanuja Ganu, and Dan Roth
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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.