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Exploring Large Language Models in Financial Argument Relation Identification

In the dynamic landscape of financial analytics, the argumentation within Earnings Conference Calls (ECCs) provides valuable insights for investors and market participants. This paper delves into the automatic relation identification between argument components in this type of data, a poorly studied task in the literature. To tackle this challenge, we empirically examined and analysed a wide range of open-source models, as well as the Generative Pre-trained Transformer GPT-4. On the one hand, our experiments in open-source models spanned general-purpose models, debate-fine-tuned models, and financial-fine-tuned models. On the other hand, we assessed the performance of GPT-4 zero-shot learning on a financial argumentation dataset (FinArg). Our findings show that a smaller open-source model, fine-tuned on relevant data, can perform as a huger general-purpose one, showing the value of enriching the local embeddings with the semantic context of data. However, GPT-4 demonstrated superior performance with F1-score of 0.81, even with no given samples or shots. In this paper, we detail our data, models and experimental setup. We also provide further performance analysis from different aspects.