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Recently, large language models (LLMs) with hundreds of billions of parameters have demonstrated the emergent ability, surpassing traditional methods in various domains even without fine-tuning over domain-specific data.
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Graph attention network for financial aspect-based sentiment classification with contrastive learning
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An optimal deep learning-based lstm for stock price prediction using twitter sentiment analysis
T Swathi, N Kasiviswanath, and A Ananda Rao · 2022
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Finetuned language models are zero-shot learners
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Generative ai agents for knowledge work augmentation in finance
Sumitra Ganesh, Leo Ardon, Daniel Borrajo, Deepeka Garg, Udari Madhushani Sehwag, Annapoorani Lakshmi Narayanan, Giuseppe Canonaco, and Manuela M Veloso · 2024
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Sentiment analysis of product reviews using machine learning and pre-trained llm
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Generative ai for end-to-end limit order book modelling: A token-level autoregressive generative model of message flow using a deep state space network
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Selective annotation makes language models better few-shot learners
Hongjin Su, Jungo Kasai, Chen Henry Wu, Weijia Shi, Tianlu Wang, Jiayi Xin, Rui Zhang, Mari Ostendorf, Luke Zettlemoyer, Noah A Smith, et al · 2023
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Gemini: a family of highly capable multimodal models
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A survey of large language models
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Enhancing product design through ai-driven sentiment analysis of amazon reviews using bert
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Knowledge editing for large language models: A survey
Song Wang, Yaochen Zhu, Haochen Liu, Zaiyi Zheng, Chen Chen, and Jundong Li · 2024
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Decision-informed neural networks with large language model integration for portfolio optimization
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