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Large Language Models (LLMs) have demonstrated impressive capabilities across diverse Natural Language Processing (NLP) tasks, including language understanding, reasoning, and generation. However, general-domain LLMs often struggle with financial tas
Externí odkaz:
http://arxiv.org/abs/2411.02476
Autor:
Fatemi, Sorouralsadat, Hu, Yuheng
Financial trading has been a challenging task, as it requires the integration of vast amounts of data from various modalities. Traditional deep learning and reinforcement learning methods require large training data and often involve encoding various
Externí odkaz:
http://arxiv.org/abs/2411.08899
Autor:
Fatemi, Sorouralsadat, Hu, Yuheng
While Large Language Models (LLMs) have shown impressive capabilities in numerous Natural Language Processing (NLP) tasks, they still struggle with financial question answering (QA), particularly when numerical reasoning is required. Recently, LLM-ba
Externí odkaz:
http://arxiv.org/abs/2410.21741
Autor:
Fatemi, Sorouralsadat, Hu, Yuheng
Financial sentiment analysis plays a crucial role in uncovering latent patterns and detecting emerging trends, enabling individuals to make well-informed decisions that may yield substantial advantages within the constantly changing realm of finance.
Externí odkaz:
http://arxiv.org/abs/2312.08725
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