2024

Revolutionizing Finance with LLMs: An Overview of Applications and Insights

Zhao, Huaqin, Liu, Zhengliang, Wu, Zihao et al.

Understand

In recent years, Large Language Models (LLMs) like ChatGPT have seen considerable advancements and have been applied in diverse fields.

Built on the Transformer architecture, these models are trained on extensive datasets, enabling them to understand and generate human language effectively.
In the financial domain, the deployment of LLMs is gaining momentum.
These models are being utilized for automating financial report generation, forecasting market trends, analyzing investor sentiment, and offering personalized financial advice.

Built on

Paulson, F.L., et al.: What makes a portfolio a portfolio?. Educational leadership 48
1991
Earlier work this paper cites.
Gage, P.: A new algorithm for data compression. C Users Journal 12
1994
Earlier work this paper cites.
Johansen, S.: Likelihood-based inference in cointegrated vector autoregressive models. OUP Oxford (1995)
1995
Earlier work this paper cites.
Nelson, B.K.: Time series analysis using autoregressive integrated moving average (arima) models. Academic emergency medicine 5
1998
Earlier work this paper cites.
Cont, R.: Empirical properties of asset returns: stylized facts and statistical issues. Quantitative finance 1
2001
Earlier work this paper cites.
Fan, J.: A selective overview of nonparametric methods in financial econometrics. Statistical Science pp. 317–337 (2005)
2005
Earlier work this paper cites.
Orderud, F.: Comparison of kalman filter estimation approaches for state space models with nonlinear measurements. In: Proc. of Scandinavian Conference on Simulation and Modeling. pp. 1–8 (2005)
2005
Earlier work this paper cites.
Brigo, D., Mercurio, F.: Interest rate models-theory and practice: with smile, inflation and credit, vol. 2. Springer (2006)
2006
Earlier work this paper cites.
Radha, S., Thenmozhi, M.: Forecasting short term interest rates using arma, arma-garch and arma-egarch models. In: Indian Institute of Capital Markets 9th Capital Markets Conference Paper (2006)
2006
Earlier work this paper cites.
Zivot, E., Wang, J.: Vector autoregressive models for multivariate time series. Modeling financial time series with S-PLUS® pp. 385–429 (2006)
2006
Earlier work this paper cites.
Ekbal, A., Bandyopadhyay, S.: Named entity recognition using support vector machine: A language independent approach. International Journal of Electrical and Computer Engineering 4
2010
Earlier work this paper cites.
Bollen, J., Mao, H., Zeng, X.: Twitter mood predicts the stock market. Journal of computational science 2
2011
Earlier work this paper cites.
Mittal, A., Goel, A.: Stock prediction using twitter sentiment analysis. Standford University, CS229 (2011 http://cs229. stanford. edu/proj2011/GoelMittal-StockMarketPredictionUsingTwitterSentimentAnalysis. pdf) 15
2012
Earlier work this paper cites.
Usha, M., Devi, M.I.: Analysis of sentiments using unsupervised learning techniques. In: 2013 International Conference on Information Communication and Embedded Systems (ICICES). pp. 241–245. IEEE (2013)
2013
Earlier work this paper cites.
Kalyanaraman, V., Kazi, S., Tondulkar, R., Oswal, S.: Sentiment analysis on news articles for stocks. In: 2014 8th Asia Modelling Symposium. pp. 10–15. IEEE (2014)
2014
Earlier work this paper cites.
Li, Q., Wang, T., Li, P., Liu, L., Gong, Q., Chen, Y.: The effect of news and public mood on stock movements. Information Sciences 278
2014
Earlier work this paper cites.
Malo, P., Sinha, A., Korhonen, P., Wallenius, J., Takala, P.: Good debt or bad debt: Detecting semantic orientations in economic texts. Journal of the Association for Information Science and Technology 65
2014
Earlier work this paper cites.
Alvarado, J.C.S., Verspoor, K., Baldwin, T.: Domain adaption of named entity recognition to support credit risk assessment. In: Proceedings of the Australasian Language Technology Association Workshop 2015. pp. 84–90 (2015)
2015
Earlier work this paper cites.
Friede, G., Busch, T., Bassen, A.: Esg and financial performance: aggregated evidence from more than 2000 empirical studies. Journal of sustainable finance & investment 5
2015
Earlier work this paper cites.
Nguyen, T.H., Shirai, K., Velcin, J.: Sentiment analysis on social media for stock movement prediction. Expert Systems with Applications 42
2015
Earlier work this paper cites.
Kirange, D., Deshmukh, R.R., et al.: Sentiment analysis of news headlines for stock price prediction. Composoft, An International Journal of Advanced Computer Technology 5
2016
Earlier work this paper cites.
Lopez-Rojas, E., Elmir, A., Axelsson, S.: Paysim: A financial mobile money simulator for fraud detection. In: 28th European Modeling and Simulation Symposium, EMSS, Larnaca. pp. 249–255. Dime University of Genoa (2016)
2016
Earlier work this paper cites.
Baker, T., Dellaert, B.: Regulating robo advice across the financial services industry. Iowa L. Rev. 103
2017
Earlier work this paper cites.
Freitag, M., Al-Onaizan, Y.: Beam search strategies for neural machine translation. arXiv preprint arXiv:1702.01806 (2017)
Original
2017
Earlier work this paper cites.
Khedr, A.E., Yaseen, N., et al.: Predicting stock market behavior using data mining technique and news sentiment analysis. International Journal of Intelligent Systems and Applications 9
2017
Earlier work this paper cites.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30
2017
Earlier work this paper cites.
Ahmadi, Z., Martens, P., Koch, C., Gottron, T., Kramer, S.: Towards bankruptcy prediction: Deep sentiment mining to detect financial distress from business management reports. In: 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA). pp. 293–302. IEEE (2018)
2018
Earlier work this paper cites.
Chen, J.M.: Models for predicting business bankruptcies and their application to banking and financial regulation. Penn St. L. Rev. 123
2018
Earlier work this paper cites.
Luo, L., Yang, Z., Yang, P., Zhang, Y., Wang, L., Lin, H., Wang, J.: An attention-based bilstm-crf approach to document-level chemical named entity recognition. Bioinformatics 34
2018
Earlier work this paper cites.
Maia, M., Handschuh, S., Freitas, A., Davis, B., McDermott, R., Zarrouk, M., Balahur, A.: Www’18 open challenge: financial opinion mining and question answering. In: Companion proceedings of the the web conference 2018. pp. 1941–1942 (2018)
2018
Earlier work this paper cites.
Radford, A., Narasimhan, K., Salimans, T., Sutskever, I., et al.: Improving language understanding by generative pre-training (2018)
2018
Earlier work this paper cites.
Roy, A., Sun, J., Mahoney, R., Alonzi, L., Adams, S., Beling, P.: Deep learning detecting fraud in credit card transactions. In: 2018 systems and information engineering design symposium (SIEDS). pp. 129–134. IEEE (2018)
2018
Earlier work this paper cites.
Shah, D., Isah, H., Zulkernine, F.: Predicting the effects of news sentiments on the stock market. In: 2018 IEEE International Conference on Big Data (Big Data). pp. 4705–4708. IEEE (2018)
2018
Earlier work this paper cites.
Araci, D.: Finbert: Financial sentiment analysis with pre-trained language models. arXiv preprint arXiv:1908.10063 (2019)
Original
2019
Earlier work this paper cites.
Buehler, H., Gonon, L., Teichmann, J., Wood, B.: Deep hedging. Quantitative Finance 19
2019
Earlier work this paper cites.
Escrig-Olmedo, E., Fernández-Izquierdo, M.Á., Ferrero-Ferrero, I., Rivera-Lirio, J.M., Muñoz-Torres, M.J.: Rating the raters: Evaluating how esg rating agencies integrate sustainability principles. Sustainability 11
2019
Earlier work this paper cites.
Kalra, S., Prasad, J.S.: Efficacy of news sentiment for stock market prediction. In: 2019 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COMITCon). pp. 491–496. IEEE (2019)
2019
Earlier work this paper cites.
Lee, C.F., Chen, H.Y., Lee, J.: Financial econometrics, mathematics and statistics. Springer (2019)
2019
Earlier work this paper cites.
Lewis, M., Liu, Y., Goyal, N., Ghazvininejad, M., Mohamed, A., Levy, O., Stoyanov, V., Zettlemoyer, L.: Bart: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension. arXiv preprint arXiv:1910.13461 (2019)
Original
2019
Earlier work this paper cites.
Mohan, S., Mullapudi, S., Sammeta, S., Vijayvergia, P., Anastasiu, D.C.: Stock price prediction using news sentiment analysis. In: 2019 IEEE fifth international conference on big data computing service and applications (BigDataService). pp. 205–208. IEEE (2019)
2019
Earlier work this paper cites.
Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. OpenAI blog 1
2019
Earlier work this paper cites.
Yu, J.H., Kang, J., Park, S.: Information availability and return volatility in the bitcoin market: analyzing differences of user opinion and interest. Information Processing & Management 56
2019
Earlier work this paper cites.
Audrino, F., Sigrist, F., Ballinari, D.: The impact of sentiment and attention measures on stock market volatility. International Journal of Forecasting 36
2020
Earlier work this paper cites.
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33
2020
Earlier work this paper cites.
Dastile, X., Celik, T., Potsane, M.: Statistical and machine learning models in credit scoring: A systematic literature survey. Applied Soft Computing 91
2020
Earlier work this paper cites.
Li, J., Sun, A., Han, J., Li, C.: A survey on deep learning for named entity recognition. IEEE Transactions on Knowledge and Data Engineering 34
2020
Earlier work this paper cites.
Li, X., Wu, P., Wang, W.: Incorporating stock prices and news sentiments for stock market prediction: A case of hong kong. Information Processing & Management 57
2020
Earlier work this paper cites.
Lourenço, C.J., Dellaert, B.G., Donkers, B.: Whose algorithm says so: The relationships between type of firm, perceptions of trust and expertise, and the acceptance of financial robo-advice. Journal of Interactive Marketing 49
2020
Earlier work this paper cites.

Similar

Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., Liu, P.J.: Exploring the limits of transfer learning with a unified text-to-text transformer. The Journal of Machine Learning Research 21
2020
Cited alongside, same era.
Sert, O.C., Şahin, S.D., Özyer, T., Alhajj, R.: Analysis and prediction in sparse and high dimensional text data: The case of dow jones stock market. Physica A: Statistical Mechanics and its Applications 545
2020
Cited alongside, same era.
Yang, Y., Uy, M.C.S., Huang, A.: Finbert: A pretrained language model for financial communications. arXiv preprint arXiv:2006.08097 (2020)
Original
2020
Cited alongside, same era.
Zheng, W., Jin, M.: The effects of class imbalance and training data size on classifier learning: an empirical study. SN Computer Science 1
2020
Cited alongside, same era.
Chen, Z., Chen, W., Smiley, C., Shah, S., Borova, I., Langdon, D., Moussa, R., Beane, M., Huang, T.H., Routledge, B., et al.: Finqa: A dataset of numerical reasoning over financial data. arXiv preprint arXiv:2109.00122 (2021)
Original
2021
Cited alongside, same era.
Ehrmann, M., Hamdi, A., Pontes, E.L., Romanello, M., Doucet, A.: Named entity recognition and classification on historical documents: A survey (2021)
2021
Cited alongside, same era.
Kim, A.G., Yoon, S.: Corporate bankruptcy prediction with domain-adapted bert. In: EMNLP 2021, 3rd Workshop on ECONLP (2021)
2021
Cited alongside, same era.
Nasar, Z., Jaffry, S.W., Malik, M.K.: Named entity recognition and relation extraction: State-of-the-art. ACM Computing Surveys (CSUR) 54
2021
Cited alongside, same era.
Yang, L., Ng, T.L.J., Smyth, B., Dong, R.: Fact check: Analyzing financial events from multilingual news sources. arXiv preprint arXiv:2106.15221 (2021)
Original
2021
Cited alongside, same era.
Zhao, L., Li, L., Zheng, X., Zhang, J.: A bert based sentiment analysis and key entity detection approach for online financial texts. In: 2021 IEEE 24th International Conference on Computer Supported Cooperative Work in Design (CSCWD). pp. 1233–1238. IEEE (2021)
2021
Cited alongside, same era.
Zumente, I., Lāce, N.: Esg rating—necessity for the investor or the company? sustainability, 13 (16), 8940 (2021)
Original
2021
Cited alongside, same era.
Cai, H., Liao, W., Liu, Z., Zhang, Y., Huang, X., Ding, S., Ren, H., Wu, Z., Dai, H., Li, S., et al.: Coarse-to-fine knowledge graph domain adaptation based on distantly-supervised iterative training. arXiv preprint arXiv:2211.02849 (2022)
Original
2022
Cited alongside, same era.

Then

Liu, X.Y., Wang, G., Yang, H., Zha, D.: Fingpt: Democratizing internet-scale data for financial large language models (2023)
2023
Later among the works it cites.
Liu, X., Li, H., Zhu, X.: A gpt-based method of automated compliance checking through prompt engineering (2023)
2023
Later among the works it cites.
Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology p. 100017 (2023)
2023
Later among the works it cites.
Liu, Z., Zhong, A., Li, Y., Yang, L., Ju, C., Wu, Z., et al.: Radiology-gpt: a large language model for radiology. arxiv [preprint]. 2023 [cited august 21, 2023]
2023
Later among the works it cites.
Liu, Z., He, X., Liu, L., Liu, T., Zhai, X.: Context matters: A strategy to pre-train language model for science education. arXiv preprint arXiv:2301.12031 (2023)
Original
2023
Later among the works it cites.
Liu, Z., Li, Y., Cao, Q., Chen, J., Yang, T., Wu, Z., Hale, J., Gibbs, J., Rasheed, K., Liu, N., et al.: Transformation vs tradition: Artificial general intelligence (agi) for arts and humanities. arXiv preprint arXiv:2310.19626 (2023)
Original
2023
Later among the works it cites.
Liu, Z., Li, Y., Shu, P., Zhong, A., Yang, L., Ju, C., Wu, Z., Ma, C., Luo, J., Chen, C., Kim, S., Hu, J., Dai, H., Zhao, L., Zhu, D., Liu, J., Liu, W., Shen, D., Liu, T., Li, Q., Li, X.: Radiology-llama2: Best-in-class large language model for radiology (2023)
2023
Later among the works it cites.
Liu, Z., Wang, P., Li, Y., Holmes, J., Shu, P., Zhang, L., Liu, C., Liu, N., Zhu, D., Li, X., Li, Q., Patel, S.H., Sio, T.T., Liu, T., Liu, W.: Radonc-gpt: A large language model for radiation oncology (2023)
2023
Later among the works it cites.
Liu, Z., Wu, Z., Hu, M., Zhao, B., Zhao, L., Zhang, T., Dai, H., Chen, X., Shen, Y., Li, S., Murray, B., Liu, T., Sikora, A.: Pharmacygpt: The ai pharmacist (2023)
2023
Later among the works it cites.
Liu, Z., Yu, X., Zhang, L., Wu, Z., Cao, C., Dai, H., Zhao, L., Liu, W., Shen, D., Li, Q., Liu, T., Zhu, D., Li, X.: Deid-gpt: Zero-shot medical text de-identification by gpt-4 (2023)
2023
Later among the works it cites.
Liu, Z., Zhong, A., Li, Y., Yang, L., Ju, C., Wu, Z., Ma, C., Shu, P., Chen, C., Kim, S., Dai, H., Zhao, L., Zhu, D., Liu, J., Liu, W., Shen, D., Li, X., Li, Q., Liu, T.: Radiology-gpt: A large language model for radiology (2023)
2023
Later among the works it cites.
Liu, Z., Zhong, A., Li, Y., Yang, L., Ju, C., Wu, Z., Ma, C., Shu, P., Chen, C., Kim, S., et al.: Radiology-gpt: A large language model for radiology. arXiv preprint arXiv:2306.08666 (2023)
Original
2023
Later among the works it cites.
Lopez-Lira, A., Tang, Y.: Can chatgpt forecast stock price movements? return predictability and large language models. arXiv preprint arXiv:2304.07619 (2023)
Original
2023
Later among the works it cites.
Luo, B., Zhang, Z., Wang, Q., Ke, A., Lu, S., He, B.: Ai-powered fraud detection in decentralized finance: A project life cycle perspective. arXiv preprint arXiv:2308.15992 (2023)
Original
2023
Later among the works it cites.
Ma, C., Wu, Z., Wang, J., Xu, S., Wei, Y., Liu, Z., Jiang, X., Guo, L., Cai, X., Zhang, S., Zhang, T., Zhu, D., Shen, D., Liu, T., Li, X.: Impressiongpt: An iterative optimizing framework for radiology report summarization with chatgpt (2023)
2023
Later among the works it cites.
Makridakis, S., Petropoulos, F., Kang, Y.: Large language models: Their success and impact. Forecasting 5
2023
Later among the works it cites.
Maple, C., Szpruch, L., Epiphaniou, G., Staykova, K., Singh, S., Penwarden, W., Wen, Y., Wang, Z., Hariharan, J., Avramovic, P.: The ai revolution: opportunities and challenges for the finance sector. arXiv preprint arXiv:2308.16538 (2023)
Original
2023
Later among the works it cites.
Nourallah, M.: One size does not fit all: Young retail investors’ initial trust in financial robo-advisors. Journal of Business Research 156
2023
Later among the works it cites.
Rezayi, S., Liu, Z., Wu, Z., Dhakal, C., Ge, B., Dai, H., Mai, G., Liu, N., Zhen, C., Liu, T., et al.: Exploring new frontiers in agricultural nlp: Investigating the potential of large language models for food applications. arXiv preprint arXiv:2306.11892 (2023)
Original
2023
Later among the works it cites.
Scherer, B., Lehner, S.: Trust me, i am a robo-advisor. Journal of Asset Management 24
2023
Later among the works it cites.
Shi, Y., Xu, S., Liu, Z., Liu, T., Li, X., Liu, N.: Mededit: Model editing for medical question answering with external knowledge bases (2023)
2023
Later among the works it cites.
Tan, C., Cao, Q., Li, Y., Zhang, J., Yang, X., Zhao, H., Wu, Z., Liu, Z., Yang, H., Wu, N., et al.: On the promises and challenges of multimodal foundation models for geographical, environmental, agricultural, and urban planning applications. arXiv preprint arXiv:2312.17016 (2023)
Original
2023
Later among the works it cites.
Tang, C., Liu, Z., Ma, C., Wu, Z., Li, Y., Liu, W., Zhu, D., Li, Q., Li, X., Liu, T., Fan, L.: Policygpt: Automated analysis of privacy policies with large language models (2023)
2023
Later among the works it cites.
Tang, R., Chuang, Y.N., Hu, X.: The science of detecting llm-generated texts. arXiv preprint arXiv:2303.07205 (2023)
Original
2023
Later among the works it cites.
Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023)
Original
2023
Later among the works it cites.
Visintin, A., Conti, M.: Leveraging social networks for mergers and acquisitions forecasting. In: International Conference on Web Information Systems Engineering. pp. 144–159. Springer (2023)
2023
Later among the works it cites.
Wang, J., Liu, Z., Zhao, L., Wu, Z., Ma, C., Yu, S., Dai, H., Yang, Q., Liu, Y., Zhang, S., Shi, E., Pan, Y., Zhang, T., Zhu, D., Li, X., Jiang, X., Ge, B., Yuan, Y., Shen, D., Liu, T., Zhang, S.: Review of large vision models and visual prompt engineering (2023)
2023
Later among the works it cites.
Wang, S., Yuan, H., Zhou, L., Ni, L.M., Shum, H.Y., Guo, J.: Alpha-gpt: Human-ai interactive alpha mining for quantitative investment (2023)
2023
Later among the works it cites.
Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: Bloomberggpt: A large language model for finance (2023)
2023
Later among the works it cites.
Wu, T., He, S., Liu, J., Sun, S., Liu, K., Han, Q.L., Tang, Y.: A brief overview of chatgpt: The history, status quo and potential future development. IEEE/CAA Journal of Automatica Sinica 10
2023
Later among the works it cites.
Wu, Z., Zhang, L., Cao, C., Yu, X., Dai, H., Ma, C., Liu, Z., Zhao, L., Li, G., Liu, W., Li, Q., Shen, D., Li, X., Zhu, D., Liu, T.: Exploring the trade-offs: Unified large language models vs local fine-tuned models for highly-specific radiology nli task (2023)
2023
Later among the works it cites.
Xie, Q., Han, W., Zhang, X., Lai, Y., Peng, M., Lopez-Lira, A., Huang, J.: Pixiu: A large language model, instruction data and evaluation benchmark for finance (2023)
2023
Later among the works it cites.
Yang, H., Liu, X.Y., Wang, C.D.: Fingpt: Open-source financial large language models. arXiv preprint arXiv:2306.06031 (2023)
Original
2023
Later among the works it cites.
Yoon, S.: Design and implementation of an llm system to improve response time for smes technology credit evaluation. International journal of advanced smart convergence 12
2023
Later among the works it cites.
Yu, X., Chen, Z., Ling, Y., Dong, S., Liu, Z., Lu, Y.: Temporal data meets llm – explainable financial time series forecasting (2023)
2023
Later among the works it cites.
Yu, Y., Li, H., Chen, Z., Jiang, Y., Li, Y., Zhang, D., Liu, R., Suchow, J.W., Khashanah, K.: Finme: A performance-enhanced large language model trading agent with layered memory and character design. arXiv preprint arXiv:2311.13743 (2023)
Original
2023
Later among the works it cites.
Zhang, B., Yang, H., Zhou, T., Ali Babar, M., Liu, X.Y.: Enhancing financial sentiment analysis via retrieval augmented large language models. In: Proceedings of the Fourth ACM International Conference on AI in Finance. pp. 349–356 (2023)
2023
Later among the works it cites.
Zhao, H., Ling, Q., Pan, Y., Zhong, T., Hu, J.Y., Yao, J., Xiao, F., Xiao, Z., Zhang, Y., Xu, S.H., et al.: Ophtha-llama2: A large language model for ophthalmology. arXiv preprint arXiv:2312.04906 (2023)
Original
2023
Later among the works it cites.
Zhao, L., Zhang, L., Wu, Z., Chen, Y., Dai, H., Yu, X., Liu, Z., Zhang, T., Hu, X., Jiang, X., et al.: When brain-inspired ai meets agi. Meta-Radiology p. 100005 (2023)
2023
Later among the works it cites.
Zhao, W.X., Zhou, K., Li, J., Tang, T., Wang, X., Hou, Y., Min, Y., Zhang, B., Zhang, J., Dong, Z., et al.: A survey of large language models. arXiv preprint arXiv:2303.18223 (2023)
Original
2023
Later among the works it cites.
Zheng, Z., Chen, K.Y., Cao, X.Y., Lu, X.Z., Lin, J.R.: Llm-funcmapper: Function identification for interpreting complex clauses in building codes via llm. arXiv preprint arXiv:2308.08728 (2023)
Original
2023
Later among the works it cites.
Zhong, T., Wei, Y., Yang, L., Wu, Z., Liu, Z., Wei, X., Li, W., Yao, J., Ma, C., Li, X., et al.: Chatabl: Abductive learning via natural language interaction with chatgpt. arXiv preprint arXiv:2304.11107 (2023)
Original
2023
Later among the works it cites.
Zhou, M., Liu, X., Liu, D., Wu, Z., Liu, Z., Zhao, L., Zhu, D., Guo, L., Han, J., Liu, T., et al.: Fine-grained artificial neurons in audio-transformers for disentangling neural auditory encoding. In: Findings of the Association for Computational Linguistics: ACL 2023. pp. 7943–7956 (2023)
2023
Later among the works it cites.
Jeong, C.: Fine-tuning and utilization methods of domain-specific llms (2024)
2024
Closest in time.
Liu, Y., He, H., Han, T., Zhang, X., Liu, M., Tian, J., Zhang, Y., Wang, J., Gao, X., Zhong, T., et al.: Understanding llms: A comprehensive overview from training to inference. arXiv preprint arXiv:2401.02038 (2024)
Original
2024
Closest in time.
Liu, Z., Zhong, A., Li, Y., Yang, L., Ju, C., Wu, Z., Ma, C., Shu, P., Chen, C., Kim, S., Dai, H., Zhao, L., Zhu, D., Liu, J., Liu, W., Shen, D., Li, Q., Liu, T., Li, X.: Tailoring large language models to radiology: A preliminary approach to llm adaptation for a highly specialized domain. In: Cao, X., Xu, X., Rekik, I., Cui, Z., Ouyang, X. (eds.) Machine Learning in Medical Imaging. pp. 464–473. Springer Nature Switzerland, Cham (2024)
2024
Closest in time.
Sun, L., Huang, Y., Wang, H., Wu, S., Zhang, Q., Gao, C., Huang, Y., Lyu, W., Zhang, Y., Li, X., et al.: Trustllm: Trustworthiness in large language models. arXiv preprint arXiv:2401.05561 (2024)
Original
2024
Closest in time.
Wang, J., Wu, Z., Li, Y., Jiang, H., Shu, P., Shi, E., Hu, H., Ma, C., Liu, Y., Wang, X., et al.: Large language models for robotics: Opportunities, challenges, and perspectives. arXiv preprint arXiv:2401.04334 (2024)
Original
2024
Closest in time.

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

Reading the bibliography…