L3iTC at the FinLLM Challenge Task: Quantization for Financial Text Classification & Summarization
<|reference_start|>L3iTC at the FinLLM Challenge Task: Quantization for Financial Text Classification & Summarization: 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.<|reference_end|>
@article{pontes2024l3itc,
    title={L3iTC at the FinLLM Challenge Task: Quantization for Financial Text
  Classification & Summarization},
    author={Elvys Linhares Pontes and Carlos-Emiliano Gonz'alez-Gallardo and
  Mohamed Benjannet and Caryn Qu and Antoine Doucet},
    journal={arXiv preprint arXiv:2408.03033},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.03033},
    primaryClass={cs.CL cs.CE}
}