Fetching the paper…
Reading the bibliography…
Generative pre-trained transformer (GPT) models have revolutionized the field of natural language processing (NLP) with remarkable performance in various tasks and also extend their power to multimodal domains.
A. E. Elo, The rating of chessplayers, past and present . Arco Pub., 1978
1978
Earlier work this paper cites.
D. Weininger, “Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules,” J. Chem. Inf. Comput. Sci. , vol. 28, no. 1, pp. 31–36, 1988. [Online]. Available: https://doi.org/10.1021/ci00057a005
1988
Earlier work this paper cites.
J. L. Elman, “Finding structure in time,” Cognitive science , vol. 14, no. 2, pp. 179–211, 1990
1990
Earlier work this paper cites.
S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural computation , vol. 9, no. 8, pp. 1735–1780, 1997
1997
Earlier work this paper cites.
2009
Earlier work this paper cites.
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg et al. , “Scikit-learn: Machine learning in python,” the Journal of machine Learning research , vol. 12, pp. 2825–2830, 2011
2011
Earlier work this paper cites.
V. Ordonez, G. Kulkarni, and T. Berg, “Im2text: Describing images using 1 million captioned photographs,” Advances in neural information processing systems , vol. 24, 2011
2011
Earlier work this paper cites.
2014
Earlier work this paper cites.
T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick, “Microsoft coco: Common objects in context,” in Computer Vision–ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part V 13 . Springer, 2014, pp. 740–755
2014
Earlier work this paper cites.
Y. Zhu, R. Kiros, R. S. Zemel, R. Salakhutdinov, R. Urtasun, A. Torralba, and S. Fidler, “Aligning books and movies: Towards story-like visual explanations by watching movies and reading books,” in 2015 IEEE International Conference on Computer Vision, ICCV 2015, Santiago, Chile, December 7-13, 2015 . IEEE Computer Society, 2015, pp. 19–27. [Online]. Available: https://doi.org/10.1109/ICCV.2015.11
2015
Earlier work this paper cites.
S. Antol, A. Agrawal, J. Lu, M. Mitchell, D. Batra, C. L. Zitnick, and D. Parikh, “Vqa: Visual question answering,” in Proceedings of the IEEE international conference on computer vision , 2015, pp. 2425–2433
2015
Earlier work this paper cites.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin, “Attention is all you need,” in Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, December 4-9, 2017, Long Beach, CA, USA , I. Guyon, U. von Luxburg, S. Bengio, H. M. Wallach, R. Fergus, S. V. N. Vishwanathan, and R. Garnett, Eds., 2017, pp. 5998–6008. [Online]. Available: https://proceedings.neurips.cc/paper/2017/hash/3f5ee243547dee91fbd053c1c4a845aa-Abstract.html
2017
Earlier work this paper cites.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” Advances in neural information processing systems , vol. 30, 2017
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
M. Joshi, E. Choi, D. S. Weld, and L. Zettlemoyer, “Triviaqa: A large scale distantly supervised challenge dataset for reading comprehension,” in Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics . Vancouver, Canada: Association for Computational Linguistics, July 2017
2017
Earlier work this paper cites.
R. Krishna, Y. Zhu, O. Groth, J. Johnson, K. Hata, J. Kravitz, S. Chen, Y. Kalantidis, L.-J. Li, D. A. Shamma et al. , “Visual genome: Connecting language and vision using crowdsourced dense image annotations,” International journal of computer vision , vol. 123, pp. 32–73, 2017
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
2018
Earlier work this paper cites.
A. Radford, K. Narasimhan, T. Salimans, I. Sutskever et al. , “Improving language understanding by generative pre-training,” 2018
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
T. Mihaylov, P. Clark, T. Khot, and A. Sabharwal, “Can a suit of armor conduct electricity? a new dataset for open book question answering,” in EMNLP , 2018
2018
Earlier work this paper cites.
P. Sharma, N. Ding, S. Goodman, and R. Soricut, “Conceptual captions: A cleaned, hypernymed, image alt-text dataset for automatic image captioning,” in Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , 2018, pp. 2556–2565
2018
Earlier work this paper cites.
N. Houlsby, A. Giurgiu, S. Jastrzebski, B. Morrone, Q. De Laroussilhe, A. Gesmundo, M. Attariyan, and S. Gelly, “Parameter-efficient transfer learning for nlp,” in International Conference on Machine Learning . PMLR, 2019, pp. 2790–2799
2019
Earlier work this paper cites.
C. Clark, K. Lee, M. Chang, T. Kwiatkowski, M. Collins, and K. Toutanova, “Boolq: Exploring the surprising difficulty of natural yes/no questions,” in Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2019, Minneapolis, MN, USA, June 2-7, 2019, Volume 1 (Long and Short Papers) , J. Burstein, C. Doran, and T. Solorio, Eds. Association for Computational Linguistics, 2019, pp. 2924–2936. [Online]. Available: https://doi.org/10.18653/v1/n19-1300
2019
Earlier work this paper cites.
2019
Earlier work this paper cites.
A. Radford, J. Wu, R. Child, D. Luan, D. Amodei, I. Sutskever et al. , “Language models are unsupervised multitask learners,” OpenAI blog , vol. 1, no. 8, p. 9, 2019
2019
Earlier work this paper cites.
2019
Earlier work this paper cites.
2019
Earlier work this paper cites.
2019
Earlier work this paper cites.
2019
Earlier work this paper cites.
D. Dua, Y. Wang, P. Dasigi, G. Stanovsky, S. Singh, and M. Gardner, “Drop: A reading comprehension benchmark requiring discrete reasoning over paragraphs,” in North American Chapter of the Association for Computational Linguistics , 2019
2019
Earlier work this paper cites.
Q. Jin, B. Dhingra, Z. Liu, W. W. Cohen, and X. Lu, “Pubmedqa: A dataset for biomedical research question answering,” in Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, EMNLP-IJCNLP 2019, Hong Kong, China, November 3-7, 2019 , K. Inui, J. Jiang, V. Ng, and X. Wan, Eds. Association for Computational Linguistics, 2019, pp. 2567–2577. [Online]. Available: https://doi.org/10.18653/v1/D19-1259
2019
Earlier work this paper cites.
T. Brown, B. Mann, N. Ryder, M. Subbiah, J. D. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell et al. , “Language models are few-shot learners,” Advances in neural information processing systems , vol. 33, pp. 1877–1901, 2020
2020
Earlier work this paper cites.
T. Wolf, L. Debut, V. Sanh, J. Chaumond, C. Delangue, A. Moi, P. Cistac, T. Rault, R. Louf, M. Funtowicz et al. , “Transformers: State-of-the-art natural language processing,” in Proceedings of the 2020 conference on empirical methods in natural language processing: system demonstrations , 2020, pp. 38–45
2020
Earlier work this paper cites.
J. Rasley, S. Rajbhandari, O. Ruwase, and Y. He, “Deepspeed: System optimizations enable training deep learning models with over 100 billion parameters,” in Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining , 2020, pp. 3505–3506
2020
Earlier work this paper cites.
Y. Bisk, R. Zellers, R. L. Bras, J. Gao, and Y. Choi, “Piqa: Reasoning about physical commonsense in natural language,” in Thirty-Fourth AAAI Conference on Artificial Intelligence , 2020
2020
Earlier work this paper cites.
C. Raffel, N. Shazeer, A. Roberts, K. Lee, S. Narang, M. Matena, Y. Zhou, W. Li, and P. J. Liu, “Exploring the limits of transfer learning with a unified text-to-text transformer,” The Journal of Machine Learning Research , vol. 21, no. 1, pp. 5485–5551, 2020
2020
Earlier work this paper cites.
2020
Earlier work this paper cites.
J. Baumgartner, S. Zannettou, B. Keegan, M. Squire, and J. Blackburn, “The pushshift reddit dataset,” in Proceedings of the international AAAI conference on web and social media , vol. 14, 2020, pp. 830–839
2020
Earlier work this paper cites.
N. Stiennon, L. Ouyang, J. Wu, D. M. Ziegler, R. Lowe, C. Voss, A. Radford, D. Amodei, and P. Christiano, “Learning to summarize from human feedback,” in NeurIPS , 2020
2020
Earlier work this paper cites.
2020
Earlier work this paper cites.
2020
Earlier work this paper cites.
D. Li, B. Hu, Q. Chen, W. Peng, and A. Wang, “Towards medical machine reading comprehension with structural knowledge and plain text,” in Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, EMNLP 2020, Online, November 16-20, 2020 , B. Webber, T. Cohn, Y. He, and Y. Liu, Eds. Association for Computational Linguistics, 2020, pp. 1427–1438. [Online]. Available: https://doi.org/10.18653/v1/2020.emnlp-main.111
2020
Earlier work this paper cites.
2021
Earlier work this paper cites.
2021
Earlier work this paper cites.
2021
Earlier work this paper cites.
K. Sakaguchi, R. L. Bras, C. Bhagavatula, and Y. Choi, “Winogrande: An adversarial winograd schema challenge at scale,” Communications of the ACM , vol. 64, no. 9, pp. 99–106, 2021
2021
Earlier work this paper cites.
S. Black, L. Gao, P. Wang, C. Leahy, and S. Biderman, “GPT-Neo: Large Scale Autoregressive Language Modeling with Mesh-Tensorflow,” Mar. 2021, If you use this software, please cite it using these metadata. [Online]. Available: https://doi.org/10.5281/zenodo.5297715
2021
Earlier work this paper cites.
2021
Earlier work this paper cites.
B. Wang and A. Komatsuzaki, “GPT-J-6B: A 6 Billion Parameter Autoregressive Language Model,” https://github.com/kingoflolz/mesh-transformer-jax , May 2021
2021
Earlier work this paper cites.
2021
Earlier work this paper cites.
R. Nakano, J. Hilton, S. Balaji, J. Wu, L. Ouyang, C. Kim, C. Hesse, S. Jain, V. Kosaraju, W. Saunders, X. Jiang, K. Cobbe, T. Eloundou, G. Krueger, K. Button, M. Knight, B. Chess, and J. Schulman, “Webgpt: Browser-assisted question-answering with human feedback,” in arXiv , 2021
2021
Earlier work this paper cites.
2021
Earlier work this paper cites.
D. Hendrycks, C. Burns, S. Kadavath, A. Arora, S. Basart, E. Tang, D. Song, and J. Steinhardt, “Measuring mathematical problem solving with the MATH dataset,” in Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1, NeurIPS Datasets and Benchmarks 2021, December 2021, virtual , J. Vanschoren and S. Yeung, Eds., 2021. [Online]. Available: https://datasets-benchmarks-proceedings.neurips.cc/paper/2021/hash/be83ab3ecd0db773eb2dc1b0a17836a1-Abstract-round2.html
2021
Earlier work this paper cites.
2021
Earlier work this paper cites.
2021
Earlier work this paper cites.
2021
Earlier work this paper cites.
D. Narayanan, M. Shoeybi, J. Casper, P. LeGresley, M. Patwary, V. Korthikanti, D. Vainbrand, P. Kashinkunti, J. Bernauer, B. Catanzaro et al. , “Efficient large-scale language model training on gpu clusters using megatron-lm,” in Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis , 2021, pp. 1–15
2021
Earlier work this paper cites.
2021
Earlier work this paper cites.
2021
Cited alongside, same era.
D. Hendrycks, C. Burns, S. Basart, A. Zou, M. Mazeika, D. Song, and J. Steinhardt, “Measuring massive multitask language understanding,” Proceedings of the International Conference on Learning Representations (ICLR) , 2021
2021
Cited alongside, same era.
2021
Cited alongside, same era.
B. Peng, E. Alcaide, Q. Anthony, A. Albalak, S. Arcadinho, H. Cao, X. Cheng, M. Chung, M. Grella, K. K. GV, X. He, H. Hou, P. Kazienko, J. Kocon, J. Kong, B. Koptyra, H. Lau, K. S. I. Mantri, F. Mom, A. Saito, X. Tang, B. Wang, J. S. Wind, S. Wozniak, R. Zhang, Z. Zhang, Q. Zhao, P. Zhou, J. Zhu, and R.-J. Zhu, “Rwkv: Reinventing rnns for the transformer era,” 2023
2023
Closest in time.
“Chatrwkv,” https://github.com/BlinkDL/ChatRWKV , 2023
2023
Closest in time.
“Moss,” https://github.com/OpenLMLab/MOSS , 2023
2023
Closest in time.
“Releasing 3B and 7B RedPajama-INCITE family of models including base, instruction-tuned & chat models,” https://www.together.xyz/blog/redpajama-models-v1 , May 2023
2023
Closest in time.
“Redpajama-data: An open source recipe to reproduce llama training dataset,” https://github.com/togethercomputer/RedPajama-Data , 2023
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2022
Cited alongside, same era.
2022
Cited alongside, same era.
L. Ouyang, J. Wu, X. Jiang, D. Almeida, C. Wainwright, P. Mishkin, C. Zhang, S. Agarwal, K. Slama, A. Ray et al. , “Training language models to follow instructions with human feedback,” Advances in Neural Information Processing Systems , vol. 35, pp. 27 730–27 744, 2022
2022
Cited alongside, same era.
Y. Wang, Y. Kordi, S. Mishra, A. Liu, N. A. Smith, D. Khashabi, and H. Hajishirzi, “Self-instruct: Aligning language model with self generated instructions,” 2022
2022
Cited alongside, same era.
J. Wei, M. Bosma, V. Y. Zhao, K. Guu, A. W. Yu, B. Lester, N. Du, A. M. Dai, and Q. V. Le, “Finetuned language models are zero-shot learners,” in The Tenth International Conference on Learning Representations, ICLR 2022, Virtual Event, April 25-29, 2022 . OpenReview.net, 2022. [Online]. Available: https://openreview.net/forum?id=gEZrGCozdqR
2022
Cited alongside, same era.
Z. Yao, R. Yazdani Aminabadi, M. Zhang, X. Wu, C. Li, and Y. He, “Zeroquant: Efficient and affordable post-training quantization for large-scale transformers,” Advances in Neural Information Processing Systems , vol. 35, pp. 27 168–27 183, 2022
2022
Cited alongside, same era.
S. Mangrulkar, S. Gugger, L. Debut, Y. Belkada, and S. Paul, “Peft: State-of-the-art parameter-efficient fine-tuning methods,” https://github.com/huggingface/peft , 2022
2022
Cited alongside, same era.
2022
Cited alongside, same era.
R. Luo, L. Sun, Y. Xia, T. Qin, S. Zhang, H. Poon, and T. Liu, “Biogpt: generative pre-trained transformer for biomedical text generation and mining,” Briefings Bioinform. , vol. 23, no. 6, 2022. [Online]. Available: https://doi.org/10.1093/bib/bbac409
2022
Cited alongside, same era.
2023
Closest in time.
“Introducing MPT-7B: A New Standard for Open-Source, Commercially Usable LLMs,” https://www.mosaicml.com/blog/mpt-7b , May 2023
2023
Closest in time.
“UAE’s Technology Innovation Institute Launches Open-Source "Falcon 40B" Large Language Model for Research & Commercial Utilization,” https://www.tii.ae/news/uaes-technology-innovation-institute-launches-open-source-falcon-40b-large-language-model , May 2023
2023
Closest in time.
I. Team, “Internlm: A multilingual language model with progressively enhanced capabilities,” https://github.com/InternLM/InternLM , 2023
2023
Closest in time.
https://github.com/baichuan-inc/Baichuan-7B , 2023
2023
Closest in time.
H. Touvron, L. Martin, K. Stone, P. Albert, A. Almahairi, Y. Babaei, N. Bashlykov, S. Batra, P. Bhargava, S. Bhosale, D. Bikel, L. Blecher, C. C. Ferrer, M. Chen, G. Cucurull, D. Esiobu, J. Fernandes, J. Fu, W. Fu, B. Fuller, C. Gao, V. Goswami, N. Goyal, A. Hartshorn, S. Hosseini, R. Hou, H. Inan, M. Kardas, V. Kerkez, M. Khabsa, I. Kloumann, A. Korenev, P. S. Koura, M.-A. Lachaux, T. Lavril, J. Lee, D. Liskovich, Y. Lu, Y. Mao, X. Martinet, T. Mihaylov, P. Mishra, I. Molybog, Y. Nie, A. Poulton, J. Reizenstein, R. Rungta, K. Saladi, A. Schelten, R. Silva, E. M. Smith, R. Subramanian, X. E. Tan, B. Tang, R. Taylor, A. Williams, J. X. Kuan, P. Xu, Z. Yan, I. Zarov, Y. Zhang, A. Fan, M. Kambadur, S. Narang, A. Rodriguez, R. Stojnic, S. Edunov, and T. Scialom, “Llama 2: Open foundation and fine-tuned chat models,” 2023
2023
Closest in time.
“Introducing qwen-7b: Open foundation and human-aligned models,” https://github.com/QwenLM/Qwen-7B , 2023
2023
Closest in time.
“Xverse-13b,” https://github.com/xverse-ai/XVERSE-13B , 2023
2023
Closest in time.
2023
Closest in time.
E. J. Wang and C. Alexiuk, “Instruct-tune llama on consumer hardware,” https://github.com/tloen/alpaca-lora , 2023
2023
Closest in time.
“Open assistant,” https://github.com/LAION-AI/Open-Assistant , 2023
2023
Closest in time.
W. Yidong, Y. Zhuohao, Z. Zhengran, Y. Linyi, H. Qiang, W. Cunxiang, C. Hao, J. Chaoya, X. Rui, W. Jindong, X. Xing, Y. Wei, Z. Shikun, and Z. Yue, “Pandalm: Reproducible and automated language model assessment,” https://github.com/WeOpenML/PandaLM , 2023
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
G. Xiao, J. Lin, M. Seznec, H. Wu, J. Demouth, and S. Han, “Smoothquant: Accurate and efficient post-training quantization for large language models,” in International Conference on Machine Learning . PMLR, 2023, pp. 38 087–38 099
2023
Closest in time.
Z. Liu, B. Oguz, C. Zhao, E. Chang, P. Stock, Y. Mehdad, Y. Shi, R. Krishnamoorthi, and V. Chandra, “Llm-qat: Data-free quantization aware training for large language models,” 2023
2023
Closest in time.
V. Lialin, N. Shivagunde, S. Muckatira, and A. Rumshisky, “Stack more layers differently: High-rank training through low-rank updates,” 2023
2023
Closest in time.
Y. Anand, Z. Nussbaum, B. Duderstadt, B. Schmidt, and A. Mulyar, “Gpt4all: Training an assistant-style chatbot with large scale data distillation from gpt-3.5-turbo,” https://github.com/nomic-ai/gpt4all , 2023
2023
Closest in time.
“Mlc llm,” https://github.com/mlc-ai/mlc-llm , 2023
2023
Closest in time.
V. A. Korthikanti, J. Casper, S. Lym, L. McAfee, M. Andersch, M. Shoeybi, and B. Catanzaro, “Reducing activation recomputation in large transformer models,” Proceedings of Machine Learning and Systems , vol. 5, 2023
2023
Closest in time.
“RedPajama, a project to create leading open-source models, starts by reproducing LLaMA training dataset of over 1.2 trillion tokens,” https://www.together.xyz/blog/redpajama , Apr. 2023
2023
Closest in time.
“Langchain,” https://github.com/hwchase17/langchain , 2023
2023
Closest in time.
“xturing,” https://github.com/stochasticai/xturing , 2023
2023
Closest in time.
“Metagpt: The multi-agent framework,” https://github.com/geekan/MetaGPT , 2023
2023
Closest in time.
2023
Closest in time.
L. Zheng, W.-L. Chiang, Y. Sheng, S. Zhuang, Z. Wu, Y. Zhuang, Z. Lin, Z. Li, D. Li, E. P. Xing, H. Zhang, J. E. Gonzalez, and I. Stoica, “Judging llm-as-a-judge with mt-bench and chatbot arena,” 2023
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
Z. Liu, W. Zhang, Y. Xia, L. Wu, S. Xie, T. Qin, M. Zhang, and T. Liu, “Molxpt: Wrapping molecules with text for generative pre-training,” in Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), ACL 2023, Toronto, Canada, July 9-14, 2023 , A. Rogers, J. L. Boyd-Graber, and N. Okazaki, Eds. Association for Computational Linguistics, 2023, pp. 1606–1616. [Online]. Available: https://aclanthology.org/2023.acl-short.138
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
Z. L. Chenghao Fan and J. Tian, “Chinese-vicuna: A chinese instruction-following llama-based model,” 2023. [Online]. Available: https://github.com/Facico/Chinese-Vicuna
2023
Closest in time.
2023
Closest in time.
Q. C. Ziang Leng and C. Li, “Luotuo: An instruction-following chinese language model, lora tuning on llama,” https://github.com/LC1332/Chinese-alpaca-lora , 2023
2023
Closest in time.
J. Yang, “Firefly,” https://github.com/yangjianxin1/Firefly , 2023
2023
Closest in time.
2023
Closest in time.
H. Wang, C. Liu, N. Xi, Z. Qiang, S. Zhao, B. Qin, and T. Liu, “Huatuo: Tuning llama model with chinese medical knowledge,” 2023
2023
Closest in time.
Y. Qin, S. Hu, Y. Lin, W. Chen, N. Ding, G. Cui, Z. Zeng, Y. Huang, C. Xiao, C. Han, Y. R. Fung, Y. Su, H. Wang, C. Qian, R. Tian, K. Zhu, S. Liang, X. Shen, B. Xu, Z. Zhang, Y. Ye, B. Li, Z. Tang, J. Yi, Y. Zhu, Z. Dai, L. Yan, X. Cong, Y. Lu, W. Zhao, Y. Huang, J. Yan, X. Han, X. Sun, D. Li, J. Phang, C. Yang, T. Wu, H. Ji, Z. Liu, and M. Sun, “Tool learning with foundation models,” 2023
2023
Closest in time.
C. Wu, S. Yin, W. Qi, X. Wang, Z. Tang, and N. Duan, “Visual chatgpt: Talking, drawing and editing with visual foundation models,” 2023
2023
Closest in time.
D. Surís, S. Menon, and C. Vondrick, “Vipergpt: Visual inference via python execution for reasoning,” 2023
2023
Closest in time.
Z. Yang, L. Li, J. Wang, K. Lin, E. Azarnasab, F. Ahmed, Z. Liu, C. Liu, M. Zeng, and L. Wang, “Mm-react: Prompting chatgpt for multimodal reasoning and action,” 2023
2023
Closest in time.
Y. Shen, K. Song, X. Tan, D. Li, W. Lu, and Y. Zhuang, “Hugginggpt: Solving ai tasks with chatgpt and its friends in hugging face,” 2023
2023
Closest in time.
2023
Closest in time.