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Time series data is essential in various applications, including climate modeling, healthcare monitoring, and financial analytics.
Roberta: A robustly optimized bert pretraining approach
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DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter
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Introduction to modern information retrieval
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BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
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Language models are few-shot learners
Brown, T.; Mann, B.; Ryder, N.; Subbiah, M.; Kaplan, J. D.; Dhariwal, P.; Neelakantan, A.; Shyam, P.; Sastry, G.; Askell, A.; et al. 2020 · 2020
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Deep attentive learning for stock movement prediction from social media text and company correlations
Sawhney, R.; Agarwal, S.; Wadhwa, A.; and Shah, R. 2020 · 2020
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What Makes Good In-Context Examples for GPT- 3 3 ?
Liu, J.; Shen, D.; Zhang, Y.; Dolan, B.; Carin, L.; and Chen, W. 2021 · 2021
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Multimodal few-shot learning with frozen language models
Tsimpoukelli, M.; Menick, J. L.; Cabi, S.; Eslami, S.; Vinyals, O.; and Hill, F. 2021 · 2021
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Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting
Wu, H.; Xu, J.; Wang, J.; and Long, M. 2021 · 2021
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Large language models are zero-shot reasoners
Kojima, T.; Gu, S. S.; Reid, M.; Matsuo, Y.; and Iwasawa, Y. 2022 · 2022
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Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?
Min, S.; Lyu, X.; Holtzman, A.; Artetxe, M.; Lewis, M.; Hajishirzi, H.; and Zettlemoyer, L. 2022 · 2022
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Can Foundation Models Wrangle Your Data?
Narayan, A.; Chami, I.; Orr, L.; and Ré, C. 2022 · 2022
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Black-box tuning for language-model-as-a-service
Sun, T.; Shao, Y.; Qian, H.; Huang, X.; and Qiu, X. 2022 · 2022
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Emergent abilities of large language models
Wei, J.; Tay, Y.; Bommasani, R.; Raffel, C.; Zoph, B.; Borgeaud, S.; Yogatama, D.; Bosma, M.; Zhou, D.; Metzler, D.; et al. 2022 · 2022
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Timesnet: Temporal 2d-variation modeling for general time series analysis
Wu, H.; Hu, T.; Liu, Y.; Zhou, H.; Wang, J.; and Long, M. 2022 · 2022
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Crossformer: Transformer utilizing cross-dimension dependency for multivariate time series forecasting
Zhang, Y.; and Yan, J. 2022 · 2022
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Achiam, J.; Adler, S.; Agarwal, S.; Ahmad, L.; Akkaya, I.; Aleman, F. L.; Almeida, D.; Altenschmidt, J.; Altman, S.; Anadkat, S.; et al. 2023 · 2023
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Anil, R.; Dai, A. M.; Firat, O.; Johnson, M.; Lepikhin, D.; Passos, A.; Shakeri, S.; Taropa, E.; Bailey, P.; Chen, Z.; et al. 2023 · 2023
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TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting
Cao, D.; Jia, F.; Arik, S. O.; Pfister, T.; Zheng, Y.; Ye, W.; and Liu, Y. 2023 · 2023
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Llm4ts: Two-stage fine-tuning for time-series forecasting with pre-trained llms
Chang, C.; Peng, W.-C.; and Chen, T.-F. 2023 · 2023
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TSMixer: An All-MLP Architecture for Time Series Forecasting
Chen, S.-A.; Li, C.-L.; Arik, S. O.; Yoder, N. C.; and Pfister, T. 2023 · 2023
Llama 2: Open foundation and fine-tuned chat models
Touvron, H.; Martin, L.; Stone, K.; Albert, P.; Almahairi, A.; Babaei, Y.; Bashlykov, N.; Batra, S.; Bhargava, P.; Bhosale, S.; et al. 2023 · 2023
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Promptcast: A new prompt-based learning paradigm for time series forecasting
Xue, H.; and Salim, F. D. 2023 · 2023
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Are transformers effective for time series forecasting?
Zeng, A.; Chen, M.; Zhang, L.; and Xu, Q. 2023 · 2023
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Prompting large language model for machine translation: A case study
Zhang, B.; Haddow, B.; and Birch, A. 2023 · 2023
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Lmsys-chat-1m: A large-scale real-world llm conversation dataset
Zheng, L.; Chiang, W.-L.; Sheng, Y.; Li, T.; Zhuang, S.; Wu, Z.; Zhuang, Y.; Li, Z.; Lin, Z.; Xing, E.; et al. 2023 · 2023
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Leveraging large language models for pre-trained recommender systems
Chu, Z.; Hao, H.; Ouyang, X.; Wang, S.; Wang, Y.; Shen, Y.; Gu, J.; Cui, Q.; Li, L.; Xue, S.; et al. 2023 · 2023
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Large language models are zero-shot time series forecasters
Gruver, N.; Finzi, M.; Qiu, S.; and Wilson, A. G. 2023 · 2023
Cited alongside, same era.
From images to textual prompts: Zero-shot visual question answering with frozen large language models
Guo, J.; Li, J.; Li, D.; Tiong, A. M. H.; Li, B.; Tao, D.; and Hoi, S. 2023 · 2023
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Tabllm: Few-shot classification of tabular data with large language models
Hegselmann, S.; Buendia, A.; Lang, H.; Agrawal, M.; Jiang, X.; and Sontag, D. 2023 · 2023
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Large models for time series and spatio-temporal data: A survey and outlook
Jin, M.; Wen, Q.; Liang, Y.; Zhang, C.; Xue, S.; Wang, X.; Zhang, J.; Wang, Y.; Chen, H.; Li, X.; et al. 2023 · 2023
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Evaluating Open-Domain Question Answering in the Era of Large Language Models
Kamalloo, E.; Dziri, N.; Clarke, C.; and Rafiei, D. 2023 · 2023
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Grounding language models to images for multimodal inputs and outputs
Koh, J. Y.; Salakhutdinov, R.; and Fried, D. 2023 · 2023
Cited alongside, same era.
Chronos: Learning the language of time series
Ansari, A. F.; Stella, L.; Turkmen, C.; Zhang, X.; Mercado, P.; Shen, H.; Shchur, O.; Rangapuram, S. S.; Arango, S. P.; Kapoor, S.; et al. 2024 · 2024
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Training audio captioning models without audio
Deshmukh, S.; Elizalde, B.; Emmanouilidou, D.; Raj, B.; Singh, R.; and Wang, H. 2024 · 2024
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Prompting large language models with speech recognition abilities
Fathullah, Y.; Wu, C.; Lakomkin, E.; Jia, J.; Shangguan, Y.; Li, K.; Guo, J.; Xiong, W.; Mahadeokar, J.; Kalinli, O.; et al. 2024 · 2024
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Empowering Time Series Analysis with Large Language Models: A Survey
Jiang, Y.; Pan, Z.; Zhang, X.; Garg, S.; Schneider, A.; Nevmyvaka, Y.; and Song, D. 2024 · 2024
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Time-LLM: Time Series Forecasting by Reprogramming Large Language Models
Jin, M.; Wang, S.; Ma, L.; Chu, Z.; Zhang, J. Y.; Shi, X.; Chen, P.-Y.; Liang, Y.; Li, Y.-F.; Pan, S.; et al. 2024 · 2024
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Can large language models reason about medical questions?
Liévin, V.; Hother, C. E.; Motzfeldt, A. G.; and Winther, O. 2024 · 2024
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iTransformer: Inverted Transformers Are Effective for Time Series Forecasting
Liu, Y.; Hu, T.; Zhang, H.; Wu, H.; Wang, S.; Ma, L.; and Long, M. 2024 · 2024
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TEST: Text prototype aligned embedding to activate LLM’s ability for time series
Sun, C.; Li, Y.; Li, H.; and Hong, S. 2024 · 2024
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Extending Large Language Models for Speech and Audio Captioning
Tang, C.; Yu, W.; Sun, G.; Chen, X.; Tan, T.; Li, W.; Lu, L.; Ma, Z.; and Zhang, C. 2024 · 2024
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Harnessing the power of llms in practice: A survey on chatgpt and beyond
Yang, J.; Jin, H.; Tang, R.; Han, X.; Feng, Q.; Jiang, H.; Zhong, S.; Yin, B.; and Hu, X. 2024 · 2024
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Frequency-domain MLPs are more effective learners in time series forecasting
Yi, K.; Zhang, Q.; Fan, W.; Wang, S.; Wang, P.; He, H.; An, N.; Lian, D.; Cao, L.; and Niu, Z. 2024 · 2024
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Large Language Models for Time Series: A Survey
Zhang, X.; Chowdhury, R. R.; Gupta, R. K.; and Shang, J. 2024 · 2024
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One fits all: Power general time series analysis by pretrained lm
Zhou, T.; Niu, P.; Sun, L.; Jin, R.; et al. 2024 · 2024
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