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Large Language Models (LLMs) are widely applied to downstream domains.
Stock movement prediction from tweets and historical prices
Yumo Xu and Shay B Cohen. 2018 · 1979
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Advantage creating and sustaining superior performance
Michael E Porter and Mark R Kramer. 1985 · 1985
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Common risk factors in the returns on stocks and bonds
Eugene F Fama and Kenneth R French. 1993 · 1993
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Investor sentiment and the cross-section of stock returns
Malcolm Baker and Jeffrey Wurgler. 2006 · 2006
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Deep learning for event-driven stock prediction
Xiao Ding, Yue Zhang, Ting Liu, and Junwen Duan. 2015 · 2015
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Human-centric justification of machine learning predictions
Or Biran and Kathleen R. McKeown. 2017 · 2017
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Enhancing stock movement prediction with adversarial training
Fuli Feng, Huimin Chen, Xiangnan He, Ji Ding, Maosong Sun, and Tat-Seng Chua. 2018 · 2018
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The advantages of the matthews correlation coefficient (mcc) over f1 score and accuracy in binary classification evaluation
Davide Chicco and Giuseppe Jurman. 2020 · 2020
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Deep attentive learning for stock movement prediction from social media text and company correlations
Ramit Sawhney, Shivam Agarwal, Arnav Wadhwa, and Rajiv Shah. 2020 · 2020
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The matthews correlation coefficient (mcc) is more reliable than balanced accuracy, bookmaker informedness, and markedness in two-class confusion matrix evaluation
Davide Chicco, Niklas Tötsch, and Giuseppe Jurman. 2021 · 2021
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Parameter-efficient legal domain adaptation
Jonathan Li, Rohan Bhambhoria, and Xiaodan Zhu. 2022 · 2022
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Training language models to follow instructions with human feedback
Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al. 2022 · 2022
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Chain-of-thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al. 2022 · 2022
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Large language models are better reasoners with self-verification
Yixuan Weng, Minjun Zhu, Fei Xia, Bin Li, Shizhu He, Shengping Liu, Bin Sun, Kang Liu, and Jun Zhao. 2022 · 2022
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Numhtml: Numeric-oriented hierarchical transformer model for multi-task financial forecasting
Linyi Yang, Jiazheng Li, Ruihai Dong, Yue Zhang, and Barry Smyth. 2022 · 2022
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Automatic chain of thought prompting in large language models
Zhuosheng Zhang, Aston Zhang, Mu Li, and Alex Smola. 2022 · 2022
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Open australian legal llm
Umar Butler. 2023 · 2023
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Chatlaw: Open-source legal large language model with integrated external knowledge bases
Jiaxi Cui, Zongjian Li, Yang Yan, Bohua Chen, and Li Yuan. 2023 · 2023
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Quzhe Huang, Mingxu Tao, Chen Zhang, Zhenwei An, Cong Jiang, Zhibin Chen, Zirui Wu, and Yansong Feng. 2023 · 2023
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Legal syllogism prompting: Teaching large language models for legal judgment prediction
Cong Jiang and Xiaolei Yang. 2023 · 2023
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Tigerscore: Towards building explainable metric for all text generation tasks
Dongfu Jiang, Yishan Li, Ge Zhang, Wenhao Huang, Bill Yuchen Lin, and Wenhu Chen. 2023 · 2023
Cited alongside, same era.
Chatdoctor: A medical chat model fine-tuned on llama model using medical domain knowledge
Yunxiang Li, Zihan Li, Kai Zhang, Ruilong Dan, and You Zhang. 2023 · 2023
Cited alongside, same era.
Beyond one-model-fits-all: A survey of domain specialization for large language models
Chen Ling, Xujiang Zhao, Jiaying Lu, Chengyuan Deng, Can Zheng, Junxiang Wang, Tanmoy Chowdhury, Yun-Qing Li, Hejie Cui, Tian yu Zhao, Amit Panalkar, Wei Cheng, Haoyu Wang, Yanchi Liu, Zhengzhang Chen, Haifeng Chen, Chris White, Quanquan Gu, Carl Yang, and Liang Zhao. 2023 · 2023
Cited alongside, same era.
Faithful chain-of-thought reasoning
Qing Lyu, Shreya Havaldar, Adam Stein, Li Zhang, Delip Rao, Eric Wong, Marianna Apidianaki, and Chris Callison-Burch. 2023 · 2023
Cited alongside, same era.
Fine-tuning and utilization methods of domain-specific llms
Cheonsu Jeong. 2024 · 2024
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Learning to generate explainable stock predictions using self-reflective large language models
Kelvin JL Koa, Yunshan Ma, Ritchie Ng, and Tat-Seng Chua. 2024 · 2024
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Biomistral: A collection of open-source pretrained large language models for medical domains
Yanis Labrak, Adrien Bazoge, Emmanuel Morin, Pierre-Antoine Gourraud, Mickael Rouvier, and Richard Dufour. 2024 · 2024
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Llama2-7b-taiwan-btc-qlora
David Lanz. 2024 · 2024
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AlphaFin: Benchmarking financial analysis with retrieval-augmented stock-chain framework
Xiang Li, Zhenyu Li, Chen Shi, Yong Xu, Qing Du, Mingkui Tan, and Jun Huang. 2024b · 2024
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Personalized stock recommendation with investors’ attention and contextual information
Takehiro Takayanagi, Kiyoshi Izumi, Atsuo Kato, Naoyuki Tsunedomi, and Yukina Abe. 2023 · 2023
Cited alongside, same era.
MAUD: An expert-annotated legal NLP dataset for merger agreement understanding
Steven Wang, Antoine Scardigli, Leonard Tang, Wei Chen, Dmitry Levkin, Anya Chen, Spencer Ball, Thomas Woodside, Oliver Zhang, and Dan Hendrycks. 2023b · 2023
Cited alongside, same era.
Bloomberggpt: A large language model for finance, 2023
Shijie Wu, Ozan Irsoy, Steven Lu, Vadim Dabravolski, Mark Dredze, Sebastian Gehrmann, Prabhanjan Kambadur, David Rosenberg, and Gideon Mann. 2024 · 2023
Cited alongside, same era.
Fingpt: Open-source financial large language models
Hongyang Yang, Xiao-Yang Liu, and Christina Dan Wang. 2023 · 2023
Cited alongside, same era.
Disc-lawllm: Fine-tuning large language models for intelligent legal services
Shengbin Yue, Wei Chen, Siyuan Wang, Bingxuan Li, Chenchen Shen, Shujun Liu, Yuxuan Zhou, Yao Xiao, Song Yun, Xuanjing Huang, et al. 2023 · 2023
Cited alongside, same era.
AI@Meta. 2024 · 2024
Cited alongside, same era.
Elephants never forget: Memorization and learning of tabular data in large language models, 2024
Sebastian Bordt, Harsha Nori, Vanessa Rodrigues, Besmira Nushi, and Rich Caruana. 2024 · 2024
Cited alongside, same era.
Large language monkeys: Scaling inference compute with repeated sampling
Bradley Brown, Jordan Juravsky, Ryan Ehrlich, Ronald Clark, Quoc V Le, Christopher Ré, and Azalia Mirhoseini. 2024 · 2024
Cited alongside, same era.
Chain of thought utilization in large language models and application in nephrology
Jing Miao, Charat Thongprayoon, Supawadee Suppadungsuk, Pajaree Krisanapan, Yeshwanter Radhakrishnan, and Wisit Cheungpasitporn. 2024 · 2024
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OpenAI. 2024 · 2024
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Learning to reason with llms
OpenAI. 2024 · 2024
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Open-o1: A model matching proprietary power with open-source innovation
OpenO1 Team. 2024 · 2024
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Mutual reasoning makes smaller llms stronger problem-solvers
Zhenting Qi, Mingyuan Ma, Jiahang Xu, Li Lyna Zhang, Fan Yang, and Mao Yang. 2024 · 2024
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Followakoinvestor: Stock recommendation by hearing voices from all kinds of investors with machine learning
Chuan Qin, Jun Chang, Wenting Tu, and Changrui Yu. 2024 · 2024
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Qwen2.5: A party of foundation models
Qwen-Team. 2024 · 2024
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" reasoning before responding": Towards legal long-form question answering with interpretability
Utkarsh Ujwal, Sai Sri Harsha Surampudi, Sayantan Mitra, and Tulika Saha. 2024 · 2024
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Math-shepherd: Verify and reinforce LLMs step-by-step without human annotations
Peiyi Wang, Lei Li, Zhihong Shao, Runxin Xu, Damai Dai, Yifei Li, Deli Chen, Yu Wu, and Zhifang Sui. 2024 · 2024
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Monte carlo tree search boosts reasoning via iterative preference learning
Yuxi Xie, Anirudh Goyal, Wenyue Zheng, Min-Yen Kan, Timothy P Lillicrap, Kenji Kawaguchi, and Michael Shieh. 2024 · 2024
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Designing heterogeneous llm agents for financial sentiment analysis
Frank Xing. 2024 · 2024
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Llava-cot: Let vision language models reason step-by-step
Guowei Xu, Peng Jin, Li Hao, Yibing Song, Lichao Sun, and Li Yuan. 2024 · 2024
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Uncovering overfitting in large language model editing
Mengqi Zhang, Xiaotian Ye, Qiang Liu, Pengjie Ren, Shu Wu, and Zhumin Chen. 2024 · 2024
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Marco-o1: Towards open reasoning models for open-ended solutions
Yu Zhao, Huifeng Yin, Bo Zeng, Hao Wang, Tianqi Shi, Chenyang Lyu, Longyue Wang, Weihua Luo, and Kaifu Zhang. 2024 · 2024
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