RA-CFGPT: Chinese financial assistant with retrieval-augmented large language model

Graphical abstract

References

[1] Lee K, Chang M W, Toutanova K. Latent retrieval for weakly supervised open domain question answering. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, 2019, 6086−6096

[2] Izacard G, Lewis P, Lomeli M, Hosseini L, Petroni F, Schick T, Dwivedi-Yu J, Joulin A, Riedel S, Grave E. Atlas: few-shot learningwith retrieval augmented language models. Journal of MachineLearning Research, 2023, 24(251): 1−43

[3] Yang H, Liu X Y, Wang C D. FinGPT: open-source financial large language models. 2023, arXiv preprint arXiv: 2306.06031

[4] Zhang X, Yang Q. XuanYuan 2.0: a large Chinese financial chat model with hundreds of billions parameters. In: Proceedings of the 32nd ACM International Conference on Information and Knowledge Management, 2023, 4435−4439

[5] Chen W, Wang Q, Long Z, Zhang X, Lu Z, Li B, Wang S, Xu J, Bai X, Huang X, Wei Z. DISC-FinLLM: a Chinese financial large language model based on multiple experts fine-tuning. 2023, arXiv preprint arXiv: 2310.15205

[6] Xiao S, Liu Z, Zhang P, Muennighoff N. C-pack: packaged resources to advance general Chinese embedding. 2023, arXiv preprint arXiv: 2309.07597

[7] Johnson J, Douze M, Jégou H. Billion-scale similarity search with GPUs. IEEE Transactions on Big Data, 2021, 7(3): 535−547

[8] Li J, Bian Y, Wang G, Lei Y, Cheng D, Ding Z, Jiang C. CFGPT: Chinese financial assistant with large language model. 2023, arXiv preprint arXiv: 2309.10654

[9] Sun T, Zhang X, He Z, Li P, Cheng Q, Yan H, Liu X, Shao Y, Tang Q, Zhao X, Chen K, Zheng Y, Zhou Z, Li R, Zhan J, Zhou Y, Li L, Yang X, Wu L, Yin Z, Huang X, Qiu X. Moss: training conversational language models from synthetic data. 2023, arXiv preprint arXiv: 2307.15020

[10] Lei Y, Li J, Jiang M, Hu J, Cheng D, Ding Z, Jiang C. CFBenchmark: Chinese financial assistant benchmark for large language model. 2023, arXiv preprint arXiv: 2311.05812

Acknowledgements

The work was supported by the National Key R&D Program of China (2022YFB4501704) and the Shanghai Science and Technology Innovation Action Plan Project (22YS1400600 and 22511100700)

Competing interests

The authors declare that they have no competing interests or financial conflicts to disclose.

RIGHTS & PERMISSIONS

2024 Higher Education Press