Page 433 - 《软件学报》2025年第4期
P. 433
邹慧琪 等: 基于图神经网络的复杂时空数据挖掘方法综述 1839
[48] Chang C, Peng WC, Chen TF. LLM4TS: Two-stage fine-tuning for time-series forecasting with pre-trained LLMs. arXiv:
2308.08469V1, 2023.
[49] Zhou T, Niu PS, Wang X, Sun L, Jin R. One fits all: Power general time series analysis by pretrained LM. In: Proc. of the 37th Conf. on
Neural Information Processing Systems. New Orleans: NeurIPS, 2024. 36.
[50] Sun CX, Li HY, Li YL, Hong SD. TEST: Text prototype aligned embedding to activate LLM’s ability for time series. In: Proc. of the
12th Int’l Conf. on Learning Representations. Vienna: OpenReview.net, 2024.
[51] Cao DF, Jia FR, Arik SÖ, Pfister T, Zheng YX, Ye W, Liu Y. TEMPO: Prompt-based generative pre-trained transformer for time series
forecasting. In: Proc. of the 12th Int’l Conf. on Learning Representations. Vienna: OpenReview.net, 2024.
[52] Gruver N, Finzi M, Qiu SK, Wilson AG. Large language models are zero-shot time series forecasters. In: Proc. of the 37th Conf. on
Neural Information Processing Systems. New Orleans: NeurIPS, 2023. 36.
[53] Xie QQ, Han WG, Lai YZ, Peng M, Huang JM. The wall street neophyte: A zero-shot analysis of ChatGPT over multimodal stock
movement prediction challenges. arXiv:2304.05351, 2023.
[54] Yu XL, Chen Z, Ling Y, Dong SJ, Liu ZY, Lu YB. Temporal data meets LLM —Explainable financial time series forecasting.
arXiv:2306.11025, 2023.
[55] Zhang BY, Yang HY, Liu XY. Instruct-FinGPT: Financial sentiment analysis by instruction tuning of general-purpose large language
models. arXiv:2306.12659, 2023.
[56] Lopez-Lira A, Tang YH. Can ChatGPT forecast stock price movements? Return predictability and large language models.
arXiv:2304.07619, 2024.
[57] Liu X, McDuff D, Kovacs G, Galatzer-Levy I, Sunshine J, Zhan JN, Poh MZ, Liao S, Di Achille P, Patel S. Large language models are
few-shot health learners. arXiv:2305.15525, 2023.
[58] Li J, Liu C, Cheng SB, Arcucci R, Hong SD. Frozen language model helps ECG zero-shot learning. In: Proc. of the 2024 Medical
Imaging with Deep Learning. Nashville: PMLR, 2024. 402–415.
[59] Xue H, Voutharoja BP, Salim FD. Leveraging language foundation models for human mobility forecasting. In: Proc. of the 30th Int’l
Conf. on Advances in Geographic Information Systems. Seattle: ACM, 2022. 90. [doi: 10.1145/3557915.356102]
[60] Kaplan J, McCandlish S, Henighan T, Brown TB, Chess B, Child R, Gray S, Radford A, Wu J, Amodei D. Scaling laws for neural
language models. arXiv:2001.08361, 2020.
[61] Zhang ZW, Li HY, Zhang ZY, Qin YJ, Wang X, Zhu WW. Graph meets LLMs: Towards large graph models. In: Proc. of the 2023
NeurIPS New Frontiers in Graph Learning Workshop. NeurIPS GLFrontiers, 2023. 1–12.
[62] Chen ZK, Mao HT, Li H, Jin W, Wen HZ, Wei XC, Wang SQ, Yin DW, Fan WQ, Liu H, Tang JL. Exploring the potential of large
language models (LLMs) in learning on graphs. ACM SIGKDD Explorations Newsletter, 2024, 25(2): 42–61. [doi: 10.1145/3655103.
3655110]
[63] He XX, Bresson X, Laurent T, Perold A, LeCun Y, Hooi B. Harnessing explanations: LLM-to-LM interpreter for enhanced text-
attributed graph representation learning. In: Proc. of the 12th Int’l Conf. on Learning Representations. Vienna: OpenReview.net, 2024.
[64] Brown TB, Mann B, Ryder N, et al. Language models are few-shot learners. In: Proc. of the 34th Conf. on Neural Information
Processing Systems. Vancouver: NeurIPS, 2020. 1877–1901.
[65] Sun TX, Shao YF, Qian H, Huang XJ, Qiu XP. Black-box tuning for language-model-as-a-service. In: Proc. of the 39th Int’l Conf. on
Machine Learning. Baltimore: PMLR, 2022. 20841–20855.
[66] He PC, Liu XD, Gao JF, Chen WZ. Deberta: Decoding-enhanced bert with disentangled attention. In: Proc. of the 9th Int’l Conf. on
Learning Representations. OpenReview.net, 2021.
[67] Ren XB, Wei W, Xia LH, Su LX, Cheng SQ, Wang JF, Yin DW, Huang C. Representation learning with large language models for
recommendation. In: Proc. of the 2024 ACM Web Conf. Singapore: ACM, 2024. 3464–3475. [doi: 10.1145/3589334.3645458]
[68] Koren Y, Rendle S, Bell R. Advances in collaborative filtering. In: Ricci F, Rokach L, Shapira B, eds. Recommender Systems
Handbook. New York: Springer, 2022. 91–142.
[69] Tang JB, Yang YH, Wei W, Shi L, Su LX, Cheng SQ, Yin DW, Huang C. GraphGPT: Graph instruction tuning for large language
models. In: Proc. of the 47th Int’l ACM SIGIR Conf. on Research and Development in Information Retrieval. Washington: ACM, 2024.
491–500. [doi: 10.1145/3626772.3657775]
[70] Yun S, Jeong M, Kim R, Kang J, Kim HJ. Graph transformer networks. In: Proc. of the 33rd Int’l Conf. on Neural Information
Processing Systems. Vancouver: Curran Associates Inc., 2019. 1073.
[71] Wei J, Wang XZ, Schuurmans D, Bosma M, Ichter B, Xia F, Chi EH, Le QV, Zhou D. Chain-of-thought prompting elicits reasoning in
large language models. In: Proc. of the 36th Int’l Conf. on Neural Information Processing Systems. New Orleans: Curran Associates