L3iTC at the FinLLM Challenge Task: Quantization for Financial Text Classification & Summarization

• URL: http://arxiv.org/abs/2408.03033v1
• Date: Tue, 6 Aug 2024 08:25:49 GMT
• Title: L3iTC at the FinLLM Challenge Task: Quantization for Financial Text Classification & Summarization
• Authors: Elvys Linhares Pontes, Carlos-Emiliano González-Gallardo, Mohamed Benjannet, Caryn Qu, Antoine Doucet,
• Abstract summary: FinLLM Challenge Task 2024 focused on two key areas: Task 1, financial text classification, and Task 2, financial text summarization. We fine-tuned several large language models (LLMs) to optimize performance for each task. Our models achieved third place for the financial classification task with an F1-score of 0.7543 and secured sixth place in the financial summarization task on the official test datasets.
• Score: 2.111699987679628
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: This article details our participation (L3iTC) in the FinLLM Challenge Task 2024, focusing on two key areas: Task 1, financial text classification, and Task 2, financial text summarization. To address these challenges, we fine-tuned several large language models (LLMs) to optimize performance for each task. Specifically, we used 4-bit quantization and LoRA to determine which layers of the LLMs should be trained at a lower precision. This approach not only accelerated the fine-tuning process on the training data provided by the organizers but also enabled us to run the models on low GPU memory. Our fine-tuned models achieved third place for the financial classification task with an F1-score of 0.7543 and secured sixth place in the financial summarization task on the official test datasets.

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