ChatGPT y GPT-4: utilidades en el sector jurídico, funcionamiento, limitaciones y riesgos de los modelos fundacionales

Autores/as

  • Francisco Julio Dosal Gómez Abogado/Graduado en Derecho por la Universidad de Cantabria/LLM en Derecho Internacional de los Negocios en el Centro de Estudios Garrigues (España) https://orcid.org/0009-0006-0506-5120
  • Judith Nieto Galende Abogada/Doble grado en Derecho y Administración de Empresas por la Universidad Autónoma de Madrid/LLM en Derecho Internacional de los Negocios en el Centro de Estudios Garrigues (España) https://orcid.org/0009-0003-8094-4449

DOI:

https://doi.org/10.51302/tce.2024.19081

Palabras clave:

ChatGPT, GPT-4, OpenAI, inteligencia artificial, tecnología legal, procesamiento del lenguaje natural, propiedad intelectual, protección de datos, innovación en la industria legal

Resumen

Los sistemas de inteligencia artificial como ChatGPT, el chatbot de OpenAI, basado en la familia de modelos de lenguaje GPT (generative pre-trained transformers), así como aquellas otras soluciones basadas en esta tecnología y ajustadas para tareas específicas, han despertado un gran interés en diversos ámbitos, entre los que se incluyen el sector legal y, particularmente, el sector de la abogacía. Sin embargo, tales modelos presentan todavía importantes limitaciones y riesgos asociados a su empleo y funcionamiento, que deben ser considerados a fin de hacer un uso adecuado y jurídicamente responsable de esta tecnología. El presente trabajo tiene por objeto aproximar a los lectores (hombres y mujeres) a la configuración, a la arquitectura y al funcionamiento de estos sistemas, así como a sus funcionalidades dentro del sector jurídico, incluyendo una revisión a sus limitaciones y riesgos jurídicos asociados, con importantes implicaciones prácticas en su aplicación.

Descargas

Los datos de descargas todavía no están disponibles.

Biografía del autor/a

Francisco Julio Dosal Gómez, Abogado/Graduado en Derecho por la Universidad de Cantabria/LLM en Derecho Internacional de los Negocios en el Centro de Estudios Garrigues (España)

Abogado especialista en derecho internacional de los negocios, arbitraje internacional y derecho internacional de la construcción. Miembro del Club Español e Iberoamericano del Arbitraje (CEIA) y del Young International Council for Commercial Arbitration (ICCA). En 2023 publicó su artículo titulado «El Dispute Avoidance Adjudication Board en la Rainbow Suite FIDIC de 2017: funcionamiento del sistema de asistencia informal y del sistema de resolución de disputas» en la Newsletter Dispute Boards del CEIA (núm. 2, pp. 25-42).

Judith Nieto Galende, Abogada/Doble grado en Derecho y Administración de Empresas por la Universidad Autónoma de Madrid/LLM en Derecho Internacional de los Negocios en el Centro de Estudios Garrigues (España)

Abogada especialista en derecho internacional de los negocios y M&A. Miembro de la International Bar Association, del Club Español e Iberoamericano del Arbitraje (CEIA) y del Young International Council for Commercial Arbitration (ICCA). Tras su paso por el área legal de M&A, actualmente trabaja en un fondo de inversiones británico especializado en energías renovables denominado WiseEnergy y cuenta con más de un año de experiencia laboral tanto a nivel nacional como internacional asesorando a clientes en el ámbito legal y financiero.

Citas

Accenture. (2021). Research Based on Analysis of Occupational Information Network.

Adams, K. (2022). ChatGPT Won't Fix Contracts. Adam on Contract Drafting. https://www.adamsdrafting.com/chatgpt-wont-fix-contracts/

Addams, G., Fabbri, A., Ladhak, F., Lehman, E. y Elhadad, N. (2023). From Sparse to Dense: GPT-4 Summarization with Chain of Density Prompting. arXiv. https://arxiv.org/abs/2309.04269

Adlakha, V., BehnamGhader, P., Han Lu, X., Meade, N. y Reddy, S. (2023). Evaluating Correctness and Faithfulness of Instruction-Following Models for Question Answering. arXiv. https://arxiv.org/pdf/2307.16877.pdf

AEPD. (2018). Informe del Gabinete Jurídico AEPD 181/2018 (N/REF: 210070/2018). https://www.aepd.es/documento/2018-0181.pdf

AEPD. (2020). Adecuación al RGPD de tratamientos que incorporan inteligencia artificial. Una introducción. https://www.aepd.es/documento/adecuacion-rgpd-ia.pdf

AEPD. (2021a). Informe del Gabinete Jurídico AEPD 81/2019 (N/REF: 028891/2019). https://www.aepd.es/documento/2019-0081.pdf

AEPD. (2021b). Informe del Gabinete Jurídico AEPD 89/2020 (N/REF: 0089/2020). https://www.aepd.es/documento/2020-0089.pdf

AEPD. (2023a). Informe del Gabinete Jurídico AEPD 52/2023 (N/REF: 0052/2023). https://www.aepd.es/documento/2023-0052.pdf

AEPD. (2023b). Inteligencia artificial: sistema vs. tratamiento, medios vs. finalidad. https://www.aepd.es/prensa-y-comunicacion/blog/inteligencia-artificial-sistema-vs-tratamiento-medio-vs-finalidad

Agencia Tributaria. (2020). Plan estratégico de la Agencia Tributaria 2020-2023.

Aletras, N., Androutsopoulos, I., Barrett, L. y Preoţiuc-Pietro, D. (Eds.). (2020). Natural legal language processing workshop 2020. CEUR Workshop Proceedings, 2.645.

Aletras, N., Ash, E., Barrett, L., Chen, D., Meyers, A., Preoţiuc-Pietro, D., Rosenberg, D. y Stent, A. (Eds.). (2019). Natural Legal Language Processing (NLLP). Proceedings of the 2019 Workshop. Association for Computational Linguistics. https://aclanthology.org/W19-22.pdf

Aletras, N., Tsarapatsanis, D., Preoţiuc-Pietro, D. y Lampos, V. (2016). Predicting judicial decisions of the European Court of Human Rights: a natural language processing perspective. PeerJ Computer Science, 2(2), 1-19.

Allen & Overy. (2023). A&O Announces Exclusive Launch Partnership with Harvey. https://www.allenovery.com/en-gb/global/news-and-insights/news/ao-announces-exclusive-launch-partnership-with-harvey

Ambrogi, B. (2023). New GPT-Based Chat App from LawDroid is a Lawyer's «Copilot» for Research, Drafting, Brainstorming and More.

Arts, S., Hou, J. y Gomez, J. C. (2021). Natural language processing to identify the creation and impact of new technologies in patent text: code, data, and new measures. Research Policy, 50(2), 1-13. https://doi.org/10.1016/j.respol.2020.104144

Bacas, T. (2022). ANALYSIS: Will ChatGPT Bring AI to Law Firms? Not Anytime Soon. Bloomberg Law. https://news.bloomberglaw.com/bloomberg-law-analysis/analysis-will-chatgpt-bring-ai-to-law-firms-not-anytime-soon

Bai, Y., Jones, A., Ndousse, K., Askell, A., Chen, A., DasSarma, N., Drain, D., Fort, S., Ganguli, D., Henighan, T., Joseph, N., Kadavath, S., Kernion, J., Conerly, T., El-Showk, S., Elhage, N., Hatfield-Dodds, Z., Hernandez, D., Hume, T., … y Kaplan, J. (2022). Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback. arXiv. https://arxiv.org/pdf/2204.05862.pdf

Beltagy, I., Peters, M. E. y Cohan, A. (2020). Longformer: The Long-Document Transformer. arXiv. https://arxiv.org/pdf/2004.05150.pdf

Bender, E. M. y Friedman, B. (2018). Data statements for natural language processing: toward mitigating system bias and enabling better science. Transactions of the Association for Computational Linguistics, 6, 587-604.

Bhaskar, A., Fabbri, A. y Durrett, G. (2023). Prompted Opinion Summarization with GPT-3.5. https://aclanthology.org/2023.findings-acl.591

Bhattacharya, P., Hiware, K., Rajgaria, S., Pochhi, N., Ghosh, K. y Ghosh, S. (2019). A comparative study of summarization algorithms applied to legal case judgments. En L. S. Azzopardi, B. Stein, N. Fuhr, P. Mayr, C. Hauff y D. Hiemstra (Eds.), Advances in Information Retrieval (ECIR), 11.437, 413-428.

Bhattacharya, P., Poddar, S., Rudra, K. y Ghosh, K. (2021). Incorporating domain knowledge for extractive summarization of legal case documents. ICAIL'21. Proceedings of the 18th International Conference on Artificial Intelligence and Law. arXiv. https://arxiv.org/pdf/2106.15876.pdf

Bommarito, M. J., Martin Katz, D. y Detterman, E. M. (2018). Lexnlp: Natural Language Processing and Information Extraction for Legal and Regulatory Texts. arXiv. https://arxiv.org/pdf/1806.03688.pdf

Bommasani, R., Hudson, D., Adeli, E., Altman, R., Arora, S., Arx, S. von, Bernstein, M. S., Bohg, J., Bosselut, A., Brunskill, E., Brynjolfsson, E., Buch, S., Card, D., Castellon, R., Chatterji, N., Chen, A., Creel, K., Quincy Davis, J., Demszky, D., … y Liang, P. (2022). On the Opportunities and Risks of Foundation Models. arXiv. https://arxiv.org/pdf/2108.07258.pdf

Bowman, S. R. (2023). Eight Things to Know about Large Language Models. arXiv. https://arxiv.org/abs/2304.00612

Branting, L. K., Pfeifer, C., Brown, B., Ferro, L., Aberdeen, J., Weiss, B., Pfaff, M. y Liao, B. (2021). Scalable and explainable legal prediction. Artificial Intelligence Law, 29, 213-238.

Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D. M., Wu, J., … y Amodei, D. (2020). Language models are few-shot learners. Advances in Neural Information Processing Systems 33 (NeurIPS 2020). Vancouver, Canadá.

Bruno, A., Mazzeo, P. L., Chetouani, A., Tliba, M. y Kerkouri, M. A. (2023). Insights into Classifying and Mitigating LLMs' Hallucinations. arXiv. https://arxiv.org/pdf/2311.08117.pdf

Bubeck, S., Chandrasekaran, V., Eldan, R., Gehrke, J., Horvitz, E., Kamar, E., Lee, P., Lee, Y. T., Li, Y., Lundberg, S., Nori, H., Palangi, H., Ribeiro, M. T. y Zhang, Y. (2023). Sparks of Artificial General Intelligence: Early Experiments with GPT-4. arXiv. https://arxiv.org/pdf/2303.12712.pdf

Burns, C., Ye, H., Klein, D. y Steinhardt, J. (2022). Discovering Latent Knowledge in Language Models without Supervision. arXiv. https://arxiv.org/pdf/2212.03827.pdf

Cao, Z., Wei, F., Li, W. y Li, S. (2017). Faithful to the Original: Fact Aware Neural Abstractive Summarization. arXiv. https://arxiv.org/pdf/1711.04434.pdf

Cath, C., Wachter, S., Mittelstadt, B., Taddeo, M. y Floridi, L. (2018). Artificial intelligence and the «good society»: the US, EU, and UK approach. Science and Engineering Ethics, 24, 505-528. https://doi.org/10.1007/s11948-017-9901-7

CBS News. (2023). Lawyers Fined for Filing Bogus Case Law Created by ChatGPT. https://www.cbsnews.com/news/chatgpt-judge-fines-lawyers-who-used-ai/

Cerullo, M. (2023a). A Lawyer Used ChatGPT to Prepare a Court Filing. It Went Horribly Awry. CBS News. https://www.cbsnews.com/news/lawyer-chatgpt-court-filing-avianca/

Cerullo, M. (2023b). Texas Judge Bans Filings Solely Created by AI after ChatGPT Made Up Cases. CBS News. https://www.cbsnews.com/news/texas-judge-bans-chatgpt-court-filing/

Chalkidis, I., Androutsopoulos, I. y Aletras, N. (2019). Neural legal judgment prediction in English. En A. Korhonen, D. Traum y L. Màrquez (Eds.), Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (pp. 4.317-4.323). Association for Computational Linguistics.

Chalkidis, I., Androutsopoulos, I. y Michos, A. (2017). Extracting contract elements. Proceedings of the 16th Edition of the International Conference on Articial Intelligence and Law (pp. 19-28).

Chalkidis, I., Androutsopoulos, I. y Michos, A. (2018). Obligation and prohibition extraction using hierarchical RNNs. En I. Gurevych y Y. Miyao (Eds.), Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Vol. 2, Short Papers, pp. 254-259). Association for Computational Linguistics.

Chalkidis, I., Fergadiotis, M., Kotitsas, S., Malakasiotis, P., Aletras, N. y Androutsopoulos, I. (2020). An empirical study on large-scale multi-label text classification including few and zero-shot labels. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (pp. 7.503-7.515). Association for Computational Linguistics.

Chalkidis, I., Fergadiotis, M., Malakasiotis, P., Aletras, N. y Androutsopoulos, I. (2020). LEGAL-BERT: The Muppets Straight out of Law School. arXiv. https://arxiv.org/pdf/2010.02559.pdf

Chalkidis, I., Fergadiotis, M., Tsarapatsanis, D., Aletras, N., Androutsopoulos, I. y Malakasiotis, P. (2021). Paragraph-level rationale extraction through regularization: a case study on European Court of Human Rights Cases. En K. Toutanova, A. Rumshisky, L. Zettlemoyer, D. Hakkani-Tur, I. Beltagy, R. Cotterell, T. Chakraboty e Y. Zhou (Eds.), Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (pp. 226-241). Association for Computational Linguistics.

Chalkidis, I., Jana, A., Hartung, D., Bommaritto, M., Androutsopoulos, I., Martin Katz, D. y Aletras, N. (2022). LexGLUE: A Benchmark Dataset for Legal Language Understanding in English. arXiv. https://arxiv.org/pdf/2110.00976v4.pdf

Chalkidis, I. y Kampas, D. (2019). Deep learning in law: early adaptation and legal word embeddings trained on large corpora. Artificial Intelligence and Law, 27, 171-198.

Chan, l., Garriga-Alonso, A., Goldowsky-Dill, N., Greenblatt, R., Nitishinskaya, J., Radhakrishnan, A. y Shlegeris, B. (2022). Causal Scrubbing: A Method for Rigorously Testing Interpretability Hypotheses [Redwood Research].

Chen, X., Li, M., Gao, X. y Zhang, X. (2022). Towards improving faithfulness in abstractive summarization. 36th Conference on Neural Information Processing Systems (NeurIPS 2022) (pp. 1-13).

Chen, Y., Sun, Y., Yang, Z. y Lin, H. (2020). Joint entity and relation extraction for legal documents with legal feature enhancement. En D. Scott, N. Bel y C. Zong (Eds.), Proceedings of the 28th International Conference on Computational Linguistics (pp. 1.561-1.571). Association for Computing Machinery.

Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Won Chung, H., Sutton, C., Gehrmann, S., Schuh, P., Shi, K., Tsvyashchenko, S., Maynez, J., Rao, A., Barnes, P., Tay, Y., Shazeer, N., Prabhakaran, V., … y Fiedel, N. (2022). PaLM: Scaling Language Modeling with Pathways, Google Research. arXiv. https://arxiv.org/pdf/2204.02311.pdf

Cobbe, K., Kosaraju, V., Bavarian, M., Chen, M., Jun, H., Kalser, L., Plappert, M., Tworek, J., Hilton, J., Nakano, R., Hesse, C. y Schulman, J. (2021). Training Verifiers to Solve Math Word Problems. arXiv. https://arxiv.org/pdf/2110.14168.pdf

Comisión Europea. (2020). Trends and Developments in Artificial Intelligence. https://ec.europa.eu/newsroom/dae/redirection/document/71193

Cuatrecasas. (2023). Cuatrecasas sella una alianza estratégica con Harvey para implantar la IA generativa. https://www.cuatrecasas.com/es/spain/art/cuatrecasas-sella-una-alianza-estrategica-con-harvey-para-implantar-la-ia-generativa

Dai, Z., Yang, Z., Yang, Y., Carbonell, J., Le, Q. V. y Salakhutdinov, R. (2019). Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context. arXiv. https://arxiv.org/pdf/1901.02860.pdf

Dev, S. y Phillips, J. (2019). Attenuating Bias in Word Vectors. arXiv. https://arxiv.org/pdf/1901.07656.pdf

Devlin, J., Chang, M.-W., Lee, K. y Toutanova, K. (2019). BERT: Pre-Training of Deep Bidirectional Transformers for Language Understanding. arXiv. https://arxiv.org/pdf/1810.04805.pdf

Elhage, N., Nanda, N., Olsson, C., Henighan, T., Joseph, N., Mann, B., Askell, A., Bai, Y., Chen, A., Conerly, T., DasSarma, N., Drain, D., Ganguli, D., Hatfield-Dodds, Z., Hernandez, D., Jones, A., Kernion, J., Lovitt, L., Ndousse, K., … y Olah, C. (2021). A mathematical framework for transformer circuits. Anthropic. https://transformer-circuits.pub/2021/framework/index.html

Etreros, J. y Sánchez, R. (2022). Responsabilidad civil e inteligencia artificial. Economic & Jurist. https://www.economistjurist.es/articulos-juridicos-destacados/responsabilidad-civil-e-inteligencia-artificial/

Expert.AI. (2023). Cuatrecasas incorpora la inteligencia artificial a sus procesos de trabajo. https://www.expert.ai/es/cuatrecasas-incorpora-la-inteligencia-artificial-a-sus-procesos-de-trabajo/

Fernandes, P., Madaan, A., Lin, E., Farinhas, A., Martins, P. H., Bertsch, A., Souza, J. G. C. de, Zhou, S., Wu, T., Neubig, G. y Martins, A. F. T. (2023). Bridging the Gap: A Survey on Integrating (Human) Feedback for Natural Language Generation. arXiv. https://arxiv.org/pdf/2305.00955.pdf

Ferrara, E. (2023). Should Chatgpt Be Biased? Challenges and Risks of Bias in Large Language Models. arXiv. https://arxiv.org/pdf/2304.03738.pdf

Ferro, L., Aberdeen, J., Branting, K., Pfeifer, C., Yeh, A. y Chakraborty, A. (2019). Scalable methods for annotating legal-decision corpora. En N. Aletras, E. Ash, L. Barrett, D. Chen, A. Meyers, D. Preotiuc-Prieto, D. Rosenber y A. Stent (Eds.), Proceedings of the Natural Legal Language Processing Workshop (pp. 12-20). Association for Computational Linguistics.

Fortune Business Insights. (2023). AI Market Size Report. https://www.fortunebusinessinsights.com/industry-reports/artificial-intelligence-market-100114

Galgani, F., Compton, P. y Hoffmann, A. (2012). Towards automatic generation of catchphrases for legal case reports. International Conference on Intelligent Text Processing and Computational Linguistics (pp. 414-425).

Gao, X., Singh, M. P. y Mehra, P. (2012). Mining business contracts for service exceptions. IEEE Transactions on Services Computing, 5(3), 333-344. IEEE.

García Vidal, Á. (2020). Propiedad intelectual y minería de textos y datos: estudio de los artículos 3 y 4 de la Directiva (UE) 2019/790. Actas de Derecho Industrial y Derecho de Autor, 40 (2019-2020) (pp. 99-124). Universidad de Santiago de Compostela.

George, C. y Stuhlmüller, A. (2023). Factored Verification: Detecting and Reducing Hallucination in Summaries of Academic Papers. arXiv. https://arxiv.org/pdf/2310.10627.pdf

Goyal, T., Li, J. J. y Durrett, G. (2023). News Summarization and Evaluation in the Era of GPT-3. arXiv. https://arxiv.org/abs/2209.12356

Grand View Research. (2023). Artificial Intelligence Market Size. https://www.grandviewresearch.com/industry-analysis/artificial-intelligence-ai-market

Guan, J., Dodge, J., Wadden, D., Huang, M. y Peng, H. (2023). Language Models Hallucinate, but May Excel at Fact Verification. arXiv. https://arxiv.org/pdf/2310.14564.pdf

Guo, Z., Schlichtkrull, M. y Vlachos, A. (2022). A survey on automated fact-checking. Transactions of the Association for Computational Linguistics, 10, 178-206.

Han, X., Zhang, Z., Ding, N., Gu, Y., Liu, X., Huo, Y., Qiu, J., Zhang, A., Zhang, L., Han, W., Huang, M., Jin, Q., Lan, Y., Liu, Y., Liu, Z., Lu, Z., Qiu, X., Song, R., Tang, J., … y Zhu, J. (2021). Pre-trained models: past, present and future. AI Open, 2, 225-250.

Hatzius, J., Briggs, J., Kodnani, D. y Pierdomenico, G. (2023). The Potentially Large Effects of Artificial Intelligence on Economic Growth (Briggs/Kodnani). Goldman Sachs. https://www.ansa.it/documents/1680080409454_ert.pdf

Hegel, A., Shah, M., Peaslee, G., Roof, B. y Elwany, E. (2021). The Law of Large Documents: Understanding the Structure of Legal Contracts Using Visual Cues. arXiv. https://arxiv.org/pdf/2107.08128.pdf

Henderson, P., Li, X., Jurafsky, D., Hashimoto, T., Lemley, M. A. y Liang, P. (2023). Foundation Models and Fair Use. arXiv. https://arxiv.org/pdf/2303.15715.pdf

Hendrycks, D., Burns, C., Chen, A. y Ball, S. (2021). CUAD: an expert-annotated NLP dataset for legal contract review. 35th Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 1). arXiv. https://arxiv.org/pdf/2103.06268.pdf

Hu, Z., Li, X., Liu, Z. y Sun, M. (2017). Few-shot charge prediction with discriminative legal attributes. En E. M. Bender, L. Derczynski y P. Isabelle (Eds.), Proceedings of the 27th International Conference on Computational Linguistics (pp. 487-498). Association for Computational Linguistics.

Huang, J. y Chang, K. C.-C. (2023). Towards reasoning in large language models: a survey. Findings of the Association for Computational Linguistics: ACL 2023 (pp. 1.049-1.065). https://aclanthology.org/2023.findings-acl.67.pdf

Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B. y Liu, T. (2023). A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions. arXiv. https://arxiv.org/pdf/2311.05232.pdf

Hugenholtz, P. B. y Quintais, J. P. (2021). Copyright and artificial creation: does EU copyright law protect AI-assisted output? IIC. International Review of Intellectual Property and Competition Law, 52, 1.190-1.216.

IEEE. (2017). The IEEE Global Initiative on Ethics of Autonomous and Intelligent Systems. https://standards.ieee.org/wp-content/uploads/import/documents/other/ead1e.pdf

Jackson, P., Al-Kofahi, K., Tyrrell, A. y Vachher, A. (2003). Information extraction from case law and retrieval of prior cases. Artificial Intelligence, 150, 239-290.

Janiesch, C., Zschech, P. y Heinrich, K. (2021). Machine Learning and Deep Learning. arXiv. https://arxiv.org/pdf/2104.05314.pdf

Jelinek, A. (2020). Preguntas frecuentes sobre la sentencia del Tribunal de Justicia de la Unión Europea en el asunto C-311/18-Comisaria de Protección de Datos vs. Facebook Irlanda y Maximillian Schrems. European Data Protection Board. https://www.aepd.es/documento/faqs-sentencia-schrems-ii-es.pdf

Ji, Z., Lee, N., Frieske,R., Yu, T., Su, D., Xu, Y., Ishii, E., Bang, Y., Dai, W., Madotto, A. y Fung, P. (2023). Survey of hallucination in natural language generation. ACM Computing Surveys, 55(12), 1-38. Association for Computing Machinery.

Kalson, Z. (2022). The implications of ChatGPT and artificial intelligence in family law. Family Lawyer Magazine. https://familylawyermagazine.com/chatgpt-and-artificial-intelligence-in-family-law/

Kandpal, N., Deng, H., Roberts, A., Wallace, E. y Raffel, C. (2023). Large language models struggle to learn long-tail knowledge. Proceedings of the 40th International Conference on Machine Learning (pp. 15.696-15.707). Association for Computing Machinery.

Kang, C. y Choi, J. (2023). Impact of Co-occurrence on Factual Knowledge of Large Language Models. arXiv. https://arxiv.org/pdf/2310.08256.pdf

Katz, D. M., Bommarito, M. J. y Blackman, J. (2017). A general approach for predicting the behavior of the Supreme Court of the United States. PLoS ONE, 12(4). https://doi.org/10.1371/journal.pone.0174698

Katz, D. M., Bommarito, M. J., Gao, S. y Arredondo, P. D. (2023). GPT-4 Passes the Bar Exam. SSRN. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4389233

Kaufman, A. R., Kraft, P. y Sen, M. (2019). Improving supreme court forecasting using boosted decision trees. Political Analysis, 27, 381-387.

Kien, P. M., Nguyen, H.T., Bach, N. X., Tran, V., Nguyen, M. L. y Phuong, T. M. (2020). Answering legal questions by learning neural attentive text representation. En D. Scott, N. Bel y C. Zong (Eds.), Proceedings of the 28th International Conference on Computational Linguistics (pp. 988-998). International Committee on Computational Linguistics.

Kojima, T., Gu, S. S., Reid, M., Matsuo, Y. e Iwasawa, Y. (2022). Large language models are zero-shot reasoners. 36th Conference on Neural Information Processing Systems (NeurIPS 2022).

Kowsrihawat, K., Vateekul, P. y Boonkwan, P. (2018). Predicting Judicial decisions of criminal cases from Thai Supreme Court using bi-directional GRU with attention mechanism. 5th Asian Conference on Defense Technology (ACDT) (pp. 50-55). IEEE.

Lee, K., Ippolito, D., Nystrom, A., Zhang, C., Eck, D., Callison-Burch, C. y Carlini, N. (2022). Deduplicating training data makes language models better. En S. Muresan, P. Nakov y A. Villavicencio (Eds.), Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Vol. 1, Long Papers, pp. 8.424-8.445). Association for Computational Linguistics.

Leivaditi, S., Rossi, J. y Kanoulas, E. (2020). A Benchmark for Lease Contract Review. arXiv. https://arxiv.org/pdf/2010.10386.pdf

Li, S., Li, X., Shang, L., Dong, Z., Sun, C., Liu, B., Ji, Z., Jiang, X. y Liu, Q. (2022). How pre-trained language models capture factual knowledge? A causal-inspired analysis. Findings of the Association for Computational Linguistics: ACL 2022 (pp. 720-1.732). Association for Computational Linguistics.

Li, Y., Li, Z., Zhang, K., Dan, R., Jiang, S. y Zhang, Y. (2023). ChatDoctor: a medical chat model fine-tuned on a Large Language Model Meta-AI (LLaMA) using medical domain knowledge. Cureus, 15(6). https://arxiv.org/ftp/arxiv/papers/2303/2303.14070.pdf

Lin, P. K. (2023). Retrofitting fair use: art & generative AI after Warhol. Santa Clara Law Review, 66, 1-31.

Lin, S., Hilton, J. y Evans, O. (2022). TruthfulQA: measuring how models mimic human falsehoods. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Vol. 1, Long Papers, pp. 3.214-3.252). Association for Computational Linguistics.

Lippi, M., Pałka, P., Contissa, G., Lagioia, F., Micklitz, H.-W. Sartor, G. y Torroni, P. (2019). CLAUDETTE: an automated detector of potentially unfair clauses in online terms of service. Artificial Intelligence and Law, 27(2), 117-139. https://link.springer.com/article/10.1007/s10506-019-09243-2

Liu, Y., Iter, D., Xu, Y., Wang, S., Xu, R. y Zhu, C. (2023). G-EVAL: NLG Evaluation Using GPT-4 with Better Human Alignment. arXiv. https://arxiv.org/pdf/2303.16634.pdf

Liu, N. F., Lin, K., Hewitt, J., Paranjape, A., Bevilcqua, M., Petroni, F. y Liang, P. (2023). Lost in the Middle: How Language Models Use Long Contexts. arXiv. https://arxiv.org/abs/2307.03172

Locke, D. y Zuccon, G. (2022). Case law retrieval: accomplishments, problems, methods and evaluations in the past 30 years. ACM Computing Surveys, 1(1), 1-37. https://arxiv.org/pdf/2202.07209.pdf

Lomas, N. (2019). Researchers Spotlight the Lie of «Anonymous» Data. TechCrunch. https://techcrunch.com/2019/07/24/researchers-spotlight-the-lie-of-anonymous-data/

Long, S., Tu, C., Liu, Z. y Sun. M. (2019). Automatic judgment prediction via legal reading comprehension. En M. Sun, X. Huang, H. Ji, Z. Liu y Y. Liu (Eds.), Chinese Computational Linguistics (Vol. 11.856, pp. 558-572).

Lovering, C. y Pavlick, E. (2022). Unit testing for concepts in neural networks. En B. Roark y A. Nenkova (Eds.), Transactions of the Association for Computational Linguistics, 10, 1.193-1.208.

Luo, B., Feng, Y., Xu, J., Zhang, X. y Zhao, D. (2017). Learning to predict charges for criminal cases with legal basis. En M. Palmer, R. Hwa y S. Riedel (Eds.), Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (pp. 2.727-2.736). Association for Computational Linguistics.

Mallen, A., Asai, A., Zhong, V., Das, R., Khashabi, D. y Hajishirzi, H. (2023). When not to trust language models: investigating effectiveness of parametric and non-parametric memories. En A. Rogers, J. Boyd-Graber y N. Okazaki (Eds.), Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Vol. 1, Long Papers, pp. 9.802-9.822). Association for Computational Linguistics.

Markovski, Y. (2023). How Your Data is Used to Improve Model Performance. OpenAI. https://help.openai.com/en/articles/5722486-how-your-data-is-used-to-improve-model-performance

Maynez, J., Narayan, S., Bohnet, B. y McDonald, R. (2020). On Faithfulness and Factuality in Abstractive Summarization. arXiv. https://arxiv.org/pdf/2005.00661.pdf

McCarthy, J., Minsky, M. L., Rochester, N. y Shannon, C. E. (1955). A proposal for the Dartmouth Summer Research Project on Artificial Intelligence. Stanford University. http://jmc.stanford.edu/articles/dartmouth/dartmouth.pdf

McKenna, N., Li, T., Cheng, L., Hosseini, M. J., Johnson, M. y Steedman, M. (2023). Sources of Hallucination by Large Language Models on Inference Tasks. arXiv. https://arxiv.org/pdf/2305.14552.pdf

Medvedeva, M., Vols, M. y Wieling, M. (2018). Judicial decisions of the European Court of Human Rights: looking into the crystal ball. Proceedings of the Conference on Empirical Legal Studies in Europe 2018 (pp. 1-24). https://martijnwieling.nl/files/Medvedeva-submitted.pdf

Medvedeva, M., Vols, M. y Wieling, M. (2020). Using machine learning to predict decisions of the European Court of Human Rights. Artificial Intelligence and Law, 28(2), 237-266.

Mencia, E. L. y Furnkranzand, J. (2010). Efficient multilabel classification algorithms for large-scale problems in the legal domain. En E. Francesconi, S. Montemagni, W. Peters y D. Tiscornia (Eds.), Semantic Processing of Legal Texts, Lecture Notes in Computer Science (Vol. 6.036, pp. 192-215). Springer.

Meng, K., Sharma, A., Andonian, A., Beclinkov, Y. y Bau, D. (2023). Mass-Editing Memory in a Transformer. arXiv. https://arxiv.org/pdf/2210.07229.pdf

Merken, S. (2023). New York Lawyers Sanctioned For Using Fake ChatGPT Cases in Legal Brief. Reuters. https://www.reuters.com/legal/new-york-lawyers-sanctioned-using-fake-chatgpt-cases-legal-brief-2023-06-22/

Min, S., Krishna, K., Lyu, X., Lewis, M., Yih, W.-T., Ko, P. W., Iyyer, M., Zettlemoyer, L. y Hajishirzi, H. (2023). FACTSCORE: Fine-Grained Atomic Evaluation of Factual Precision in Long Form Text Generation. https://arxiv.org/pdf/2305.14251.pdf

Moore, P. V. (2023). Inteligencia artificial en el entorno laboral. Desafíos para los trabajadores. OpenMind BBVA. https://www.bbvaopenmind.com/articulos/inteligencia-artificial-en-entorno-laboral-desafios-para-trabajadores/

Mumcuoğlu, E., Öztürk, C. E. y Ozaktas, H. M. (2021). Natural language processing in law: prediction of outcomes in the higher courts of Turkey. Information Processing & Management, 58(5). https://doi.org/10.1016/j.ipm.2021.102684

Nallapati, R. y Manning, C. D. (2008). Legal docket-entry classification: where machine learning stumbles. Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing (pp. 438-446). Association for Computational Linguistics.

Navarro, E. (2023). How can ChatGPT impact legal services? Consejo General de la Abogacía Española.

Niklaus, J., Chalkidis, I. y Stürmer, M. (2021). Swiss-Judgment-Prediction: A Multilingual Legal Judgment Prediction Benchmark. arXiv. https://arxiv.org/pdf/2110.00806.pdf

Nye, M., Andreassen, A. J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C. y Odena, A. (2022). Show Your Work: Scratchpads for Intermediate Computation with Language Models. arXiv. https://arxiv.org/pdf/2112.00114.pdf

OCDE. (2019). Recommendation of the Council on OECD Legal Instruments Artificial Intelligence.

OMPI. (2020). Versión revisada del documento temático sobre las políticas de propiedad intelectual y la inteligencia artificial.

OMPI. (2021). WIPO Conversation on Intellectual Property (IP) and Artificial Intelligence (AI): Third Session.

Onoe, Y., Zhang, M., Choi, E. y Durrett, G. (2022). Entity cloze by date: what LMs know about unseen entities. En M. Carpuat, M.-C. de Marneffe e I. V. Meza Ruiz (Eds.), Findings of the Association for Computational Linguistics: NAACL 2022 (pp. 693-702).

OpenAI. (s. f.). OpenAI Personal Data Removal Request.

OpenAI. (2019). Request for Comment on Intellectual Property Protection for Artificial Intelligence Innovation, PTO-C-2019-0038. United States Patent and Trademark Office. Department of Commerce.

OpenAI. (2023a). Condiciones de uso. https://openai.com/policies/terms-of-use

OpenAI. (2023b). Custom Instructions for Chat-GPT. https://openai.com/blog/custom-instructions-for-chatgpt

OpenAI. (2023c). GPT-4 Technical Report. arXiv. https://arxiv.org/pdf/2303.08774.pdf

OpenAI. (2023d). Política de privacidad. https://openai.com/policies/privacy-policy

OpenAI. (2023e). Política de privacidad para la UE. https://openai.com/es/policies/eu-privacy-policy

OpenAI. (2024a). Enterprise Privacy at OpenAI. https://openai.com/enterprise-privacy

OpenAI. (2024b). Usage Policies. https://openai.com/policies/usage-policies

OpenAI. (2024c). How ChatGPT and Our Language Models Are Developed. https://help.openai.com/en/articles/7842364-how-chatgpt-and-our-language-models-are-developed

OpenAI. (2024d). OpenAI Personal Data Removal Request. https://share.hsforms.com/1UPy6xqxZSEqTrGDh4ywo_g4sk30

OpenAI. (2024e). OpenAI Privacy Request Portal. https://privacy.openai.com/policies?name=open-ai-privacy-request-portal#privacy-practices

OpenAI. (2024f). Data Processing Addendum.https://openai.com/policies/data-processing-addendum

Ouyang, L., Wu, J., Almeida, D., Wainwright, C. L., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., Schulman, J., Hilton, J., Kelton, F., Miller, L., Simens, M., Askell, A., Welinder, P., Christiano, P., Leike, J. y Lowe, R. (2022). Training Language Models to Follow Instructions with Human Feedback. arXiv. https://arxiv.org/pdf/2203.02155.pdf

Parlamento Europeo. (2020). Resolución del Parlamento Europeo, de 20 de octubre de 2020, sobre los derechos de propiedad intelectual para el desarrollo de las tecnologías relativas a la inteligencia artificial. https://www.europarl.europa.eu/doceo/document/TA-9-2020-0277_ES.html

Patil, V., Hase, P. y Bansal, M. (2023). Can Sensitive Information Be Deleted from LLMs? Objectives for Defending Against Extraction Attacks. arXiv. https://arxiv.org/pdf/2309.17410.pdf

Perlman, A. (2023). The Implications of ChatGPT for Legal Services and Society. Center on the Legal Profession. Harvard Law School. https://clp.law.harvard.edu/knowledge-hub/magazine/issues/generative-ai-in-the-legal-profession/the-implications-of-chatgpt-for-legal-services-and-society/

Pu, D. y Demberg, V. (2023). ChatGPT vs. human-authored text: insights into controllable text summarization and sentence style transfer. En V. Padmakumar, G. Vallejo y Y. Fu (Eds.), Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (pp. 1-18). Association for Computational Linguistic. https://aclanthology.org/2023.acl-srw.1/

PwC. (2023). PwC Announces Strategic Alliance with Harvey, Positioning PWC's Legal Business Solutions at the Forefront of Legal Generative AI. https://www.pwc.com/gx/en/news-room/press-releases/2023/pwc-announces-strategic-alliance-with-harvey-positioning-pwcs-legal-business-solutions-at-the-forefront-of-legal-generative-ai.html

Radford, A., Wu, J., Child, R., Amodei, D. y Sutskever, I. (2019). Language Models are Unsupervised Multitask Learners. https://insightcivic.s3.us-east-1.amazonaws.com/language-models.pdf

Rajani, N. F., McCann, B., Xiong, C. y Socher, R. (2019). Explain yourself! Leveraging language models for commonsense reasoning. En A. Korhonen, D. Traum y L. Màrquez (Eds.), Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (pp. 4.932-4.942).

Raji, I. D., Smart, A., White, R. N., Mitchell, M., Gebru, T., Hutchinson, B., Smith-Loud, J., Theron, D. y Barners, P. (2020). Closing the AI Accountability Gap: Defining an End-to-End Framework for Internal Algorithmic Auditing. arXiv. https://arxiv.org/pdf/2001.00973.pdf

Ravichander, A., Black, A. W., Wilson, S., Norton, T. y Sadeh, N. (2019). Question answering for privacy policies: combining computational and legal perspectives. En K. Iniu, J. Jiang, V. Ng y X. Wan (Eds.), Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) (pp. 4.947-4.958). Association for Computational Linguistics.

Rincón, G. (2023). El uso de la inteligencia artificial por la Administración Tributaria: ¿quién vigila a los vigilantes? Garrigues. https://www.garrigues.com/es_ES/garrigues-digital/uso-inteligencia-artificial-administracion-tributaria-quien-vigila-vigilantes

Roberts, G. (2022). AI Training Datasets: The Books1+Books2 that Big AI Eats for Breakfast. Vision of Freedom. https://gregoreite.com/drilling-down-details-on-the-ai-training-datasets/

Ruger, T. W., Kim, P. T., Martin, A. D. y Quinn, K. M. (2004). The Supreme Court forecasting project: legal and political science approaches to Supreme Court decision-making. Columbia Law Review, 104(4),1.150-1.210.

Sánchez, L. (2023). Francesc Muñoz: «Estoy convencido de que la IA Generativa hará a los abogados mejores». Economist & Jurist. https://www.economistjurist.es/zbloque-1/francesc-munoz-estoy-convencido-de-que-la-ia-generativa-hara-a-los-abogados-mejores/

Sánchez Aristi, R., Pérez Marcilla, M. y Andoni Eguiluz, J. (2023). El desarrollo de sistemas de inteligencia artificial y la posible infracción de derechos de autor. Cuatrecasas. https://www.cuatrecasas.com/es/spain/art/el-desarrollo-de-sistemas-de-inteligencia-artificial-y-la-posible-infraccion-de-derechos-de-autor

Sartor, G. (2020). The Impact of the General Data Protection Regulation (GDPR) on Artificial Intelligence. European Parliamentary Research Service.

Savelka, J., Gray, M. A. y Westermann, H. (2023). Explaining Legal Concepts with Augmented Large Language Models (GPT-4). arXiv. https://arxiv.org/pdf/2306.09525.pdf

Schulman, J., Wolski, F., Dhariwal, P., Radford, A. y Klimov, O. (2017). Proximal Policy Optimization Algorithms. arXiv. https://arxiv.org/pdf/1707.06347.pdf

Sellick, M. (2022). Can AI Replace Patent Attorneys? HGF. https://www.hgf.com/news/can-ai-replace-patent-attorneys/

Silva, D. de y Alahakoon, D. (2021). An Artificial Intelligence Life Cycle: From Conception to Production. arXiv. https://arxiv.org/pdf/2108.13861.pdf

Singhal, K., Tu, T., Gottweis, J., Sayres, R., Wulczyn, E., Hou, L., Clark, K., Pfohl, S., Cole-Lewis, H., Neal, D., Schaekermann, M., Wang, A., Amin, M., Lachgar, S., Mansfield, P., Prakash, S., Green, B., Dominowska, E., Aguera y Arcas, B., … y Natarajan, V. (2023). Towards Expert-Level Medical Question Answering with Large Language Models. arXiv. https://arxiv.org/pdf/2305.09617.pdf

Strickson, B. e Iglesia, B. de la. (2020). Legal judgement prediction for UK Courts. ICISS '20: Proceedings of the 3rd International Conference on Information Science and Systems. Association for Computing Machinery.

Şulea, O.-M.ª, Zampieri, M., Vela, M. y Genabith, J. van. (2017). Predicting the law area and decisions of french supreme court cases. En R. Mitkov y G. Angelova (Eds.), Proceedings of the International Conference Recent Advances in Natural Language Processing (RANLP 2017) (pp. 716-722). Incoma.

Thompson, A. (2022). What's in my AI? LifeArchitect. https://lifearchitect.ai/whats-in-my-ai/

Tiersma, P. M. (1999). Legal Language. The University of Chicago Press.

Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., Bikel, D., Blecher, L., Canton Ferrer, C., Chen, M., Cucurull, G., Esiobu, D., Fernandes, J., Fu, J., Fu, W., … y Scialom, T. (2023). Llama 2: Open Foundation and Fine-Tuned Chat Models. arXiv. https://arxiv.org/pdf/2307.09288.pdf

Tran, V., Le Nguyen, M. y Satoh, K. (2019). Building legal case retrieval systems with lexical matching and summarization using a pre-trained phrase scoring model. Proceedings of the Seventeenth International Conference on Artificial Intelligence and Law, ICAIL '19 (pp. 275-282).

Tribunal de Justicia de la Unión Europea. (16 de julio de 2009). Infopaq International A/S y Danske Dagblades Forening, C5/08.

Tuggener, D., Däniken, P. von, Peetz, T. y Cieliebak, M. (2020). LEDGAR: a large-scale multi-label corpus for text classification of legal provisions in contracts. En N. Calzoni, F. Béchet, P. Blache, K. Choukri, C. Cieri, T. Declerck, S. Goggi, H. Isahara, B. Maegaard, J. Mariani, H. Mazo, A. Moreno, J. Odijk y S. Piperidis (Eds.), Proceedings of the Twelfth Language Resources and Evaluation Conference (pp. 1.235-1.241). European Language Resources Association.

United States Court Appeals. (13 de septiembre de 1989). SOS, Inc. v. Payday, Inc. 886 F.2d 1081 (9th Cir. 1989).

United States Court of Appeals. (6 de febrero de 2002). Kelly v. Arriba Soft Corp. 280 F.3d 934 (9th Cir. 2002).

United States District Court. (19 de septiembre de 2023). Author's Guild v. OpenAI Inc. (1:23-cv-08292). Southern District of New York.

Urchs, S., Mitrovic, J. y Granitzer, M. (2021). Design and implementation of german legal decision corpora. En A. P. Rocha, L. Steel y J. van den Herik (Eds.), Proceedings of the 13th International Conference on Agents and Artificial Intelligence, ICAART (Vol. 2, pp. 515-521).

USCO. (2022). Second Request for Reconsideration for Refusal to Register A Recent Entrance to Paradise (Correspondence ID 1-3ZPC6C3; SR # 1-7100387071). https://www.copyright.gov/rulings-filings/review-board/docs/a-recent-entrance-to-paradise.pdf

USCO. (2023a). Copyright Registration Guidance: Works Containing Material Generated by Artificial Intelligence. Library of Congress.

USCO. (2023b). Zarya of the Dawn (# VAu001480196). https://www.copyright.gov/docs/zarya-of-the-dawn.pdf

Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N. y Kaiser, Ł. (2017). Attention is all you need. 31st Conference on Neural Information Processing Systems (NIPS 2017). arXiv. https://arxiv.org/pdf/1706.03762.pdf

Virtucion, M. B., Aborot, J. A., Abonita, J. K., Aviñate, R., Copino, R. J. B., Neverida, M. P., Osiana, V. O., Peramo, E. C., Syjuco, J. G. y Tan, G. B. A. (2018). Predicting decisions of the philippine supreme court using natural language processing and machine learning. 2018 IEEE 42nd Annual Computer Software and Applications Conference (COMPSAC) (pp. 130-135). IEEE.

Wang, X., Wei, J., Schuurmans, D., Le, Q., Chi, E. H., Narang, S., Chowdhery, A. y Zhou, D. (2023). Self-Consistency Improves Chain of Thought Reasoning in Language Models. arXiv. https://arxiv.org/pdf/2203.11171.pdf

Wei, J., Bosma, M., Zhao, V. Y., Guu, K., Wei Yu, A., Lester, B., Du, N., Dai, A. M. y Le, Q. V. (2022). Finetuned Language Models are Zero-Shot Learners. https://openreview.net/pdf?id=gEZrGCozdqR

Wei, J., Wang, X., Schuurman, D., Bosma, M., Ichter, B., Xia, F., Chi, E. H., Le, Q. V. y Zhou, D. (2022). Chain-of-Thought Prompting Elicits Reasoning in Large Language Models. arXiv. https://arxiv.org/pdf/2201.11903.pdf

White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J. y Schmidt, D. C. (2023). A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT. arXiv. https://arxiv.org/pdf/2302.11382.pdf

White, J., Hays, S., Fu, Q., Spencer-Smith, J. y Schmidt, D. C. (2023). ChatGPT Prompt Patterns for Improving Code Quality, Refactoring, Requirements Elicitation, and Software Design. arXiv. https://arxiv.org/pdf/2303.07839.pdf

Williams, C. (2005). Tradition and Change in Legal English. Verbal Constructions in Prescriptive Texts. Peter Lang Publishing.

World Economic Forum. (2023). Satya Nadella Says AI Golden Age Is Here and «It's Good for Humanity». https://www.weforum.org/press/2023/01/satya-nadella-says-ai-golden-age-is-here-and-it-s-good-for-humanity

Xiao, C., Zhong, H., Guo, Z., Tu, C., Liu, Z., Sun, M., Feng, Y., Han, X., Hu, Z., Wang, H. y Xu, J. (2018). CAIL2018: A Large-Scale Legal Dataset for Judgment Prediction. arXiv. https://arxiv.org/pdf/1807.02478.pdf

Xu, C., Sun, Q., Zheng, K., Geng, X., Zhao, P., Feng, J., Tao, C., Lin, Q. y Jiang, D. (2023). WizardLM: Empowering Large Language Models to Follow Complex Instructions. arXiv. https://arxiv.org/pdf/2304.12244.pdf

Yang, W., Jia, W., Zhou, X. y Luo, Y. (2019). Legal judgment prediction via multi-perspective bi-feedback network. Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence (IJCAI-19) (pp. 4.085-4.091).

Ye, H., Jiang, X., Luo, Z. y Chao, W. (2018). Interpretable charge predictions for criminal cases: learning to generate court views from fact descriptions. Proceedings of NAACL-HLT 2018 (pp. 1.854-1.864). https://aclanthology.org/N18-1168.pdf

Ye, H., Liu, T., Zhang, A., Hua, W. y Jia, W. (2023). Cognitive Mirage: A Review of Hallucinations in Large Language Models. arXiv. https://arxiv.org/pdf/2309.06794.pdf

Yu, F., Quartey, L. y Schilder, F. (2022). Legal Prompting: Teaching a Language Model to Think Like a Lawyer. https://arxiv.org/pdf/2212.01326.pdf

Zaheer, M., Guruganesh, G., Dubey, A., Ainslie, J., Alberti, C., Ontanon, S., Pham, P., Ravula, A., Wang, Q., Yang, Li y Ahmed, A. (2021 ). Big bird: transformers for longer sequences. 34th Conference on Neural Information Processing Systems (pp. 17.283-17.297). arXiv. https://arxiv.org/pdf/2007.14062.pdf

Zahn, M. (2023). Authors' lawsuit against Open-AI Could «Fundamentally Reshape» Artificial Intelligence, According to Experts. ABC News. https://abcnews.go.com/Technology/authors-lawsuit-openai-fundamentally-reshape-artificial-intelligence-experts/story?id=103379209

Zelikman, E., Wu, Y., Mu, J. y Goodman, N. D. (2022). STaR: Bootstrapping Reasoning with Reasoning. https://arxiv.org/abs/2203.14465

Zeng, A., Liu, X., Du, Z., Wang, Z., Lai, H., Ding, M., Yang, Z., Zheng, W., Xia, X., Tam, W. L., Ma, Z., Xue, Y., Zhai, J., Chen, W., Liu, Z., Zhang, P., Dong, Y. y Tang, J. (2023). GLM-130B: an open bilingual pre-trained model. The Eleventh International Conference on Learning Representations, ICLR 2023. https://openreview.net/pdf?id=-Aw0rrrPUF

Zhang, S., Dong, L., Li, X., Zhang, S., Sun, X., Wang, S., Li, J., Hu, R., Zhang, T., Wu, F. y Wang, G. (2023). Instruction Tuning for Large Language Models: A Survey. https://arxiv.org/pdf/2308.10792.pdf

Zhang, B. H., Lemoine, B. y Mitchell, M. (2018). Mitigating Unwanted Biases with Adversarial Learning. https://arxiv.org/pdf/1801.07593.pdf

Zhang, Y., Li, Y., Cui, L., Cai, D., Liu, L., Fu, T., Huang, X., Zhao, E., Zhang, Y., Chen, Y., Wang, L., Luu, A. T., Bi, W., Shi, F. y Shi, S. (2023). Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Models. arXiv. https://arxiv.org/pdf/2309.01219.pdf

Zhang, M., Press, O., Merrill, W., Liu, A. y Smith, N. A. (2023). How Language Model Hallucinations Can Snowball. arXiv. https://arxiv.org/pdf/2305.13534.pdf

Zhang, S., Roller, S., Goyal, N., Artetxe, M., Chen, M., Chen, S., Dewan, C., Diab, M., Li, X., Lin, X. V., Mihaylov, T., Ott, M., Shleifer, S., Shuster, K., Simig, D., Koura, P. S., Sridhar, A., Wang, T. y Zettlemoyer, L. (2022). OPT: Open Pre-trained Transformer Language Models. https://arxiv.org/pdf/2205.01068.pdf

Zheng, S., Huang, J. y Chan, K. C.-C. (2023). Why Does ChatGPT Fall Short in Providing Truthful Answers? https://arxiv.org/pdf/2304.10513.pdf

Zhong, H., Guo, Z., Tu, C., Xiao, C., Liu, Z. y Sun, M. (2018). Legal judgment prediction via topological learning. En E. Riloff, D. Chiang, J. Hockenmaier y J. Tsujii (Eds.), Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (pp. 3.540-3.549). Association for Computational Linguistics.

Zhong, H., Wang, Y., Tu, C., Zhang, T., Liu, Z. y Sun, M. (2020). Iteratively questioning and answering for interpretable legal judgment prediction. Proceedings of the AAAI Conference on Artificial Intelligence, 34(01), 1.250-1.257.

Zhong, H., Xiao, C., Tu, C., Zhang, T., Liu, Z. y Sun, M. (2020). How does NPL benefit legal system: a summary of legal artificial intelligence. En D. Jurafsky, J. Chai, N. Schluter y J. Tetreault (Eds.), Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (pp. 5.218-5230). Association for Computational Linguistics.

Descargas

Publicado

15-03-2024

Cómo citar

Dosal Gómez, F. J., & Nieto Galende, J. (2024). ChatGPT y GPT-4: utilidades en el sector jurídico, funcionamiento, limitaciones y riesgos de los modelos fundacionales. Revista Tecnología, Ciencia Y Educación, (28). https://doi.org/10.51302/tce.2024.19081