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| Tree of thoughts: Deliberate problem solving with large language models | Princeton University / Google DeepMind | Yao, Shunyu; Yu, Dian; Zhao, Jeffrey; Shafran, Izhak; Griffiths, Thomas L.; Cao, Yuan; Narasimhan, Karthik | https://doi.org/10.48550/arXiv.2305.10601 | [1], [10], [14], [15], [16], [17], [11] |
| Retrieval-augmented generation for knowledge-intensive NLP tasks | Facebook AI Research (Meta) / University College London / NYU | Lewis, Patrick; Perez, Ethan; Piktus, Aleksandra; Petroni, fab; Karpukhin, Vladimir; Goyal, Naman; Küttler, Heinrich; Lewis, Mike et al. | https://doi.org/10.48550/arXiv.2005.11401 | [20], [1], [21], [10], [15] |
| Self-refine: Iterative refinement with self-feedback | Carnegie Mellon University / Allen Institute for AI | Madaan, Aman; Tandon, Niket; Gupta, Prakhar; Hallinan, Skyler; Gao, Luyu; Wiegreffe, Sarah; Alon, Uri; Dziri, Nouha et al. | https://doi.org/10.48550/arXiv.2303.17651 | [1], [10], [15] |
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| ReAct: Synergizing Reasoning and Acting in Language Models | Google Research / Princeton University | Yao, Shunyu; Zhao, Jeffrey; Yu, Dian; Du, Nan; Shafran, Izhak; Narasimhan, Karthik; Cao, Yuan | https://arxiv.org/abs/2210.03629 | [14], [16], [25] |
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| QLoRA: Efficient Finetuning of Quantized LLMs | University of Washington / BitsAndBytes | Dettmers, Tim; Pagnoni, Artidoro; Holtzman, Ari; Zettlemoyer, Luke | https://arxiv.org/abs/2305.14314 | [20], [26], [7] |
| Language Models are Unsupervised Multitask Learners (GPT-2) | OpenAI | Radford, Alec; Wu, Jeffrey; Child, Rewon; Luan, David; Amodei, Dario; Sutskever, Ilya | https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf | [4], [10], [12], [13] |
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| Meta-Harness: End-to-End Optimization of Model Harnesses | Stanford, MIT, KRAFTON | Lee, Yoonho; Nair, Roshen; Zhang, Qizheng; Lee, Kangwook; Khattab, Omar; Finn, Chelsea | https://arxiv.org/abs/2507.19457 | [21] |
| PaLM: Scaling language modeling with pathways | Google Research | Chowdhery, Aakanksha; Narang, Sharan; Devlin, Jacob et al. | https://doi.org/10.48550/arXiv.2204.02311 | [1] |
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| XLNet: Generalized Autoregressive Pretraining for Language Understanding | Google / Carnegie Mellon University | Yang, Zhilin; Dai, Zihang; Yang, Yiming; Carbonell, Jaime; Salakhutdinov, Ruslan; Le, Quoc V. | https://arxiv.org/abs/1906.08237 | [9] |
| RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control | Google DeepMind | Brohan, Anthony; Brown, Noah; Carbajal, Justice; Chebotar, Yevgen; Chen, Xi et al. | https://arxiv.org/abs/2307.15818 | [28] |
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| Rephrase and respond: Let large language models ask better questions for themselves | UCLA | Deng, Yihe; Zhang, Weitong; Chen, Zixiang; Gu, Quanquan | https://doi.org/10.48550/arXiv.2311.04205 | [1] |
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| A Survey of Large Language Models | University of China, Beijing | Zhao, Wayne Xin et al. | arXiv:2303.18223 | [4] |
| On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? | University of Washington | Bender, Emily M.; Gebru, Timnit; McMillan-Major, Angelina; Shmitchell, Shmargaret | https://s10251.pcdn.co/pdf/2021-bender-parrots.pdf | [4] |
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| TinyVLA: Toward Fast, Data-Efficient Vision-Language-Action Models | East China Normal University | Wen, Junjie; Zhu, Yichen et al. | arXiv:2409.12514 | [28] |
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| Segment Anything | Meta AI | Kirillov, Alexander; Mintun, Eric; Ravi, Nikhila; Mao, Hanzi et al. | arXiv:2304.02643 | [10] |
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| Guía de prompting | Universidad de Barcelona | Rubén Alcaraz-Martínez | doi.org/10.5281/zenodo.19605053 | |
| Repositorio del proyecto (TFG) | UPC | Eduardo Cid Pérez | github.com/ItzGeness/TFG | |
| Universidad de Sevilla | Pedro Delgado Jiménez | | |
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| Authoring Agent Skills: A Software-Engineering Approach | University College London | Giuseppe Destefanis | arXiv:2607.25032 | |