THaLLE: Text Hyperlocally Augmented Large Language Extension -- Technical Report
KBTG Labs, Danupat Khamnuansin, Atthakorn Petchsod, Anuruth Lertpiya,, Pornchanan Balee, Thanawat Lodkaew, Tawunrat Chalothorn, Thadpong, Pongthawornkamol, Monchai Lertsutthiwong

TL;DR
This paper introduces THaLLE, an 8-billion-parameter LLM fine-tuned for financial analysis, achieving top performance on CFA mock exams and providing a dataset for evaluating financial advising capabilities.
Contribution
The work presents a specialized financial extension of a hyperlocally augmented LLM, with detailed fine-tuning methods and a new dataset for financial evaluation.
Findings
THaLLE outperforms comparable models on CFA mock exams
Fine-tuning techniques enhance financial reasoning in LLMs
Introduces Flare CFA dataset for financial model evaluation
Abstract
Recent advancements in Large Language Models (LLMs) have revealed new capabilities and opportunities across the technological landscape. However, the practicality of very large LLMs is challenged by their high compute cost, which does not justify the benefits given their limited capability compared to humans. While smaller, more practical LLMs have shown potential in financial analysis, though they are not yet fully proficient, as evidenced by their near-passing performance on the Chartered Financial Analyst (CFA) exam. In this work, we present Financial Analyst Extension to our Text Hyperlocally Augmented Large Language Extension (THaLLE), a series of 8B LLMs consistently achieving highest performance on mock CFA exams against models of comparable size. We thoroughly document the fine-tuning techniques used to facilitate future research. Additionally, we introduce the use of Flare CFA,…
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Taxonomy
TopicsNatural Language Processing Techniques
