JPMorgan Study: FinBert Beats GPT-4 in Financial Text Analytics
A recent study conducted by JPMorgan and Queen’s University has highlighted the superiority of FinBert, a language model specifically designed for financial text analytics, over other renowned models such as Gpt-3.5-turbo and GPT-4. The study focused on various tasks, ranging from everyday math to complex financial calculations, to determine the effectiveness of these models.
In the domain of sentiment analysis, FinBert outperformed ChatGPT, demonstrating its ability to comprehend the nuanced expressions and impact of financial news on investors. This proficiency stems from FinBert’s fine-tuning process, which aligns it closely with the financial sector’s specific terminology, language structures, and concepts. Unlike ChatGPT, FinBert does not require extensive adaptation or fine-tuning to excel in sentiment analysis tasks.
Moreover, FinBert showcased exceptional skills in arithmetic reasoning, even competing with human experts in this domain. The study revealed that FinBert’s highly specialized nature played a significant role in achieving such impressive results. On the other hand, ChatGPT, although a powerful model, failed to match the expertise demonstrated by FinBert in financial tasks.
The study also evaluated the models’ performance in financial named entity recognition (NER) and sentiment analysis, where a deep understanding of domain-specific knowledge is vital. Both ChatGPT and GPT-4 struggled to grasp the intricacies of financial terminologies, exposing a gap in their capabilities compared to fine-tuned models like FinBert and FinQANet, which are tailored for the financial sector.
While the study acknowledges the potential of large language models (LLMs) like GPT-4, it emphasizes that they are not yet on par with their specialized counterparts. The findings suggest that further enhancements are required to bridge the gap between state-of-the-art generative language models and domain-specific proficiency.
Rajiv Shah, a machine learning engineer at Hugging Face, exprèssed his views on LinkedIn, stating, A domain-specific model like FinBERT is more accurate for finance tasks than GPT-4. This perspective from the industry highlights the value and accuracy of models like FinBert in the field of finance.
The results of this study open avenues for further improvement and refinement in language models for finance-related tasks. While the potential for these models is evident, there is still work to be done to fully harness their capabilities in the context of the financial domain.
Overall, the JPMorgan and Queen’s University study sheds light on the significant advantages that FinBert offers in financial text analytics compared to Gpt-3.5-turbo and GPT-4. As the field continues to evolve, the quest for even more advanced language models tailored specifically for the financial sector continues.
