LLM Based Multi-agent Generation of Semi-structured Documents from Semantic Templates in the Public Administration Domain

Author:

Musumeci EmanueleORCID,Brienza MicheleORCID,Suriani VincenzoORCID,Nardi DanieleORCID,Bloisi Domenico DanieleORCID

Publisher

Springer Nature Switzerland

Reference16 articles.

1. Bsharat, S.M., Myrzakhan, A., Shen, Z.: Principled instructions are all you need for questioning LLaMA-1/2, GPT-3.5/4 (2024)

2. Chen, J., Lin, H., Han, X., Sun, L.: Benchmarking large language models in retrieval-augmented generation (2023). arXiv preprint arXiv:2309.01431

3. Cobbe, J., Singh, J.: Artificial intelligence as a service: legal responsibilities, liabilities, and policy challenges. Comput. Law Secur. Rev. 42, 105573 (2021)

4. Hegselmann, S., Buendia, A., Lang, H., Agrawal, M., Jiang, X., Sontag, D.: TabLLM: few-shot classification of tabular data with large language models. In: Ruiz, F., Dy, J., van de Meent, J.W. (eds.) In: Proceedings of The 26th International Conference on Artificial Intelligence and Statistics. Proceedings of Machine Learning Research, vol. 206, pp. 5549–5581. PMLR (2023). https://proceedings.mlr.press/v206/hegselmann23a.html

5. Kojima, T., Gu, S.S., Reid, M., Matsuo, Y., Iwasawa, Y.: Large language models are zero-shot reasoners. In: Koyejo, S., Mohamed, S., Agarwal, A., Belgrave, D., Cho, K., Oh, A. (eds.) Advances in Neural Information Processing System, vol. 35, pp. 22199–22213. Curran Associates, Inc. (2022)

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