Agentic AI

Materiales de la charla "Agentes inteligentes"

Referencias importantes en el tema (selección no exhaustiva)

1. Zhang, J., Xu, X., Zhang, N., Liu, R., Hooi, B., & Deng, S. (2024). Exploring Collaboration Mechanisms for LLM Agents: A Social Psychology View. arXiv preprint arXiv:2310.02124. https://doi.org/10.48550/arXiv.2310.02124
2. Chen, J., Saha, S., & Bansal, M. (2024). ReConcile: Round-Table Conference Improves Reasoning via Consensus among Diverse LLMs. En Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (pp. 7066–7085). Association for Computational Linguistics. https://doi.org/10.18653/v1/2024.acl-long.381
3. Chuang, Y.-S., Goyal, A., Harlalka, N., Suresh, S., Hawkins, R., Yang, S., Shah, D., Hu, J., & Rogers, T. T. (2024). Simulating opinion dynamics with networks of LLM-based agents. Findings of the Association for Computational Linguistics: NAACL 2024, 3326–3346. https://doi.org/10.18653/v1/2024.findings-naacl.211
4. Ferraro, A., Galli, A., La Gatta, V., Postiglione, M., Orlando, G. M., Russo, D., Riccio, G., Romano, A., & Moscato, V. (2024). Agent-Based Modelling Meets Generative AI in Social Network Simulations. Proceedings of the 2024 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). https://arxiv.org/abs/2411.16031
5. Triem, H., & Ding, Y. (2024). “Tipping the Balance”: Human Intervention in Large Language Model Multi-Agent Debate. Proceedings of the Association for Information Science and Technology, 61(1), 361–373. https://doi.org/10.1002/pra2.1034
6. Khattab, O., Singhvi, A., Maheshwari, P., Zhang, Z., Santhanam, K., Vardhamanan, S., Haq, S., Sharma, A., Joshi, T. T., Moazam, H., Miller, H., Zaharia, M., & Potts, C. (2024). DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines. The Twelfth International Conference on Learning Representations. https://arxiv.org/abs/2310.03714
7. Wei, J., Wang, X., Schuurmans, D., Bosma, M., Ichter, B., Xia, F., Chi, E., Le, Q., & Zhou, D. (2022). Chain-of-Thought Prompting Elicits Reasoning in Large Language Models, Advances in Neural Information Processing Systems, 35, 24824–24837. https://arxiv.org/abs/2201.11903
8. Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K., & Cao, Y. (2023). ReAct: Synergizing Reasoning and Acting in Language Models, ICLR 2023, https://arxiv.org/pdf/2210.03629
9. Zheng, L., Chiang, W., Sheng, Y., Zhuang, S., Wu, Z., Zhuang, Y., Lin, Z., Li, Z., Li, D., Xing, E., Zhang, H., Gonzalez, J., & Stoica, I. (2023). Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena, Advances in Neural Information Processing Systems, 36, 46595 - 46623. https://arxiv.org/abs/2306.05685
10. Shinn, N., Cassano, F., Gopinath, A., Narasimhan, K. & Yao, S. (2023). Reflexion: Language Agents with Verbal Reinforcement Learning, Advances in Neural Information Processing Systems, 36, 8634 - 8652. https://arxiv.org/abs/2303.11366
11. Wang, X., Wei, J., Schuurmans, D., Le, Q., Chi, E., Narang, S., Chowdhery, A., & Zhou, D. (2023). Self-Consistency Improves Chain of Thought Reasoning in Language Models, ICLR 2023, https://arxiv.org/abs/2203.11171
12. Liang, T., He, Z., Jiao, W., Wang, X., Wang, Y., Wang, R., Yang, Y., Shi, S., & Tu, Z. (2024). Encouraging Divergent Thinking in Large Language Models through Multi-Agent Debate, EMNLP 2024 – Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, https://arxiv.org/abs/2305.19118
13. Du, Y., Li, S., Torralba, A., Tenenbaum, J., & Mordatch, I. (2024). Improving Factuality and Reasoning in Language Models through Multiagent Debate, ICLR 2024, https://arxiv.org/abs/2305.14325
14. Li, G., Hammoud, A., Itani, H., Khizbullin, D., & Ghanem, B. (2023). CAMEL: Communicative Agents for “Mind” Exploration of Large Language Model Society, Advances in Neural Information Processing Systems, 36, https://arxiv.org/pdf/2303.17760

Material preparado por Marcelo Mendoza (mailto:[email protected])