ChatGPT y GPT-4: utilidades en el sector jurídico, funcionamiento, limitaciones y riesgos de los modelos fundacionales
DOI:
https://doi.org/10.51302/tce.2024.19081Palabras clave:
ChatGPT, GPT-4, OpenAI, inteligencia artificial, tecnología legal, procesamiento del lenguaje natural, propiedad intelectual, protección de datos, innovación en la industria legalResumen
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.
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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.
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