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Large language models (LLMs) have significantly impacted human society, influencing various domains.
Generating long sequences with sparse transformers
Child, R., Gray, S., Radford, A., & Sutskever, I. (2019) · 1904
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Megatron-LM: Training multi-billion parameter language models using model parallelism
Shoeybi, M., Patwary, M., Puri, R., LeGresley, P., Casper, J., & Catanzaro, B. (2019) · 1909
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Fine-tuning language models from human preferences
Ziegler, D. M., Stiennon, N., Wu, J., Brown, T. B., Radford, A., Amodei, D., Christiano, P., & Irving, G. (2019) · 1909
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More is different: Broken symmetry and the nature of the hierarchical structure of science
Anderson, P. W. (1972) · 1972
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Scaling laws for neural language models
Kaplan, J., McCandlish, S., Henighan, T., Brown, T. B., Chess, B., Child, R., Gray, S., Radford, A., Wu, J., & Amodei, D. (2020) · 2001
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GLU variants improve Transformer
Shazeer, N. (2020) · 2002
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A neural probabilistic language model
Bengio, Y., Ducharme, R., Vincent, P., & Janvin, C. (2003) · 2003
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Japanese and Korean voice search
Schuster, M., & Nakajima, K. (2012) · 2012
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Guidelines for snowballing in systematic literature studies and a replication in software engineering
Wohlin, C. (2014) · 2014
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MXNet: A flexible and efficient machine learning library for heterogeneous distributed systems
Chen, T., Li, M., Li, Y., Lin, M., Wang, N., Wang, M., Xiao, T., Xu, B., Zhang, C., & Zhang, Z. (2016) · 2016
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Neural machine translation of rare words with subword units
Sennrich, R., Haddow, B., & Birch, A. (2016) · 2016
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Scientific misconduct hurts
Laine, C. (2017) · 2017
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Outrageously large neural networks: The Sparsely-Gated Mixture-of-Experts layer
Shazeer, N., Mirhoseini, A., Maziarz, K., Davis, A., Le, Q., Hinton, G., & Dean, J. (2017) · 2017
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Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017) · 2017
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Gaussian Error Linear Units (GELUs)
Hendrycks, D., & Gimpel, K. (2018) · 2018
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Improving language understanding by generative pre-training
Radford, A., Narasimhan, K., Salimans, T., & Sutskever, I. (2018) · 2018
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Adaptive input representations for neural language modeling
Baevski, A., & Auli, M. (2019) · 2019
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BERT: Pre-training of deep bidirectional transformers for language understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019) · 2019
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Unified language model pre-training for natural language understanding and generation
Dong, L., Yang, N., Wang, W., Wei, F., Liu, X., Wang, Y., Gao, J., Zhou, M., & Hon, H.-W. (2019) · 2019
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Parameter-efficient transfer learning for NLP
Houlsby, N., Giurgiu, A., Jastrzebski, S., Morrone, B., Laroussilhe, Q. D., Gesmundo, A., Attariyan, M., & Gelly, S. (2019) · 2019
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DistilBERT, a distilled version of BERT: Smaller, faster, cheaper and lighter
Sanh, V., Debut, L., Chaumond, J., & Wolf, T. (2019) · 2019
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Language models are few-shot learners
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., 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., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., & Amodei, D. (2020) · 2020
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Artificial intelligence to help meet global demand for high-quality, objective peer-review in publishing
Frontiers (2020) · 2020
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Exploring the limits of transfer learning with a unified text-to-text Transformer
Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., & Liu, P. J. (2020) · 2020
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ZeRO: Memory optimizations toward training trillion parameter models
Rajbhandari, S., Rasley, J., Ruwase, O., & He, Y. (2020) · 2020
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DeepSpeed: System optimizations enable training deep learning models with over 100 billion parameters
Rasley, J., Rajbhandari, S., Ruwase, O., & He, Y. (2020) · 2020
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Between words and characters: A brief history of open-vocabulary modeling and tokenization in NLP
Mielke, S. J., Alyafeai, Z., Salesky, E., Raffel, C., Dey, M., Gallé, M., Raja, A., Si, C., Lee, W. Y., & Sagot, B. (2021) · 2021
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FlashAttention: Fast and memory-efficient exact attention with IO-awareness
Dao, T., Fu, D., Ermon, S., Rudra, A., & Ré, C. (2022) · 2022
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LoRA: Low-rank adaptation of large language models
Hu, E. J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., & Chen, W. (2022) · 2022
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Automatic analysis of available source code of top artificial intelligence conference papers
Lin, J., Wang, Y., Yu, Y., Zhou, Y., Chen, Y., & Shi, X. (2022) · 2022
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Introducing ChatGPT
OpenAI (2022) · 2022
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Train short, test long: Attention with linear biases enables input length extrapolation
Press, O., Smith, N., & Lewis, M. (2022) · 2022
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What language model architecture and pretraining objective works best for zero-shot generalization?
Wang, T., Roberts, A., Hesslow, D., Scao, T. L., Chung, H. W., Beltagy, I., Launay, J., & Raffel, C. (2022) · 2022
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Can we automate scientific reviewing?
Yuan, W., Liu, P., & Neubig, G. (2022) · 2022
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FAQ: Can I use AI writing assistants to write my review?
ACL (2023) · 2023
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ACM peer review policy
ACM (2023) · 2023
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ChatGPT and the future of journal reviews: A feasibility study
Biswas, S., Dobaria, D., & Cohen, H. L. (2023) · 2023
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Vicuna: An open-source chatbot impressing GPT-4 with 90% ChatGPT quality
Chiang, W.-L., Li, Z., Lin, Z., Sheng, Y., Wu, Z., Zhang, H., Zheng, L., Zhuang, S., Zhuang, Y., Gonzalez, J. E., Stoica, I., & Xing, E. P. (2023) · 2023
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Hello Dolly: Democratizing the magic of ChatGPT with open models
Conover, M., Hayes, M., Mathur, A., Meng, X., Xie, J., Wan, J., Ghodsi, A., Wendell, P., & Zaharia, M. (2023) · 2023
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How ChatGPT and other AI tools could disrupt scientific publishing
Conroy, G. (2023) · 2023
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NLPeer: A unified resource for the computational study of peer review
Dycke, N., Kuznetsov, I., & Gurevych, I. (2023) · 2023
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Guidance for authors, peer reviewers, and editors on use of AI, language models, and chatbots
Flanagin, A., Kendall-Taylor, J., & Bibbins-Domingo, K. (2023) · 2023
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Leveraging large language models: Transforming scholarly publishing for the better
Fortier, L. A. (2023) · 2023
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Koala: A dialogue model for academic research
Geng, X., Gudibande, A., Liu, H., Wallace, E., Abbeel, P., Levine, S., & Song, D. (2023) · 2023
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Automatic analysis of substantiation in scientific peer reviews
Guo, Y., Shang, G., Rennard, V., Vazirgiannis, M., & Clavel, C. (2023) · 2023
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Fighting reviewer fatigue or amplifying bias? Considerations and recommendations for use of ChatGPT and other large language models in scholarly peer review
Hosseini, M., & Horbach, S. P. J. M. (2023) · 2023
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Jiang, A. Q., Sablayrolles, A., Mensch, A., Bamford, C., Chaplot, D. S., Casas, D. d. l., Bressand, F., Lengyel, G., Lample, G., & Saulnier, L. (2023) · 2023
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ChatGPT for good? On opportunities and challenges of large language models for education
Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., Hüllermeier, E., Krusche, S., Kutyniok, G., Michaeli, T., Nerdel, C., Pfeffer, J., Poquet, O., Sailer, M., Schmidt, A., Seidel, T., Stadler, M., Weller, J., Kuhn, J., & Kasneci, G. (2023) · 2023
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When reviewers lock horns: Finding disagreements in scientific peer reviews
Kumar, S., Ghosal, T., & Ekbal, A. (2023) · 2023
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OpenAssistant conversations - Democratizing large language model alignment
Köpf, A., Kilcher, Y., von Rütte, D., Anagnostidis, S., Tam, Z. R., Stevens, K., Barhoum, A., Nguyen, D., Stanley, O., Nagyfi, R., Es, S., Suri, S., Glushkov, D., Dantuluri, A., Maguire, A., Schuhmann, C., Nguyen, H., & Mattick, A. (2023) · 2023
Cited alongside, same era.
Best practices for using AI tools as an author, peer reviewer, or editor
Leung, T. I., de Azevedo Cardoso, T., Mavragani, A., & Eysenbach, G. (2023) · 2023
Cited alongside, same era.
Summarizing multiple documents with conversational structure for meta-review generation
Li, M., Hovy, E., & Lau, J. (2023) · 2023
Cited alongside, same era.
Holistic evaluation of language models
Liang, P., Bommasani, R., Lee, T., Tsipras, D., Soylu, D., Yasunaga, M., Zhang, Y., Narayanan, D., Wu, Y., Kumar, A., Newman, B., Yuan, B., Yan, B., Zhang, C., Cosgrove, C., Manning, C. D., Re, C., Acosta-Navas, D., Hudson, D. A., Zelikman, E., Durmus, E., Ladhak, F., Rong, F., Ren, H., Yao, H., WANG, J., Santhanam, K., Orr, L., Zheng, L., Yuksekgonul, M., Suzgun, M., Kim, N., Guha, N., Chatterji, N. S., Khattab, O., Henderson, P., Huang, Q., Chi, R. A., Xie, S. M., Santurkar, S., Ganguli, S., Hashimoto, T., Icard, T., Zhang, T., Chaudhary, V., Wang, W., Li, X., Mai, Y., Zhang, Y., & Koreeda, Y. (2023) · 2023
Cited alongside, same era.
Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context
Gemini Team (2024) · 2024
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Gemma: Open models based on gemini research and technology
Gemma Team (2024) · 2024
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Introducing Command R+: A scalable LLM built for business
Gomez, A. (2024) · 2024
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Automated paper screening for clinical reviews using large language models: Data analysis study
Guo, E., Gupta, M., Deng, J., Park, Y.-J., Paget, M., & Naugler, C. (2024) · 2024
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Parameter-efficient fine-tuning for large models: A comprehensive survey
Han, Z., Gao, C., Liu, J., Zhang, J., & Zhang, S. Q. (2024) · 2024
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Automated scholarly paper review: Concepts, technologies, and challenges
Lin, J., Song, J., Zhou, Z., Chen, Y., & Shi, X. (2023a) · 2023
Cited alongside, same era.
Prompt engineering in large language models
Marvin, G., Hellen, N., Jjingo, D., & Nakatumba-Nabende, J. (2023) · 2023
Cited alongside, same era.
AI in peer review: Publishing’s panacea or a pandora’s box of problems?
Nath, K. A., Conway, M., & Fonseca, R. (2024) · 2023
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ChatReviewer
Ni, S. (2023) · 2023
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GPT-4, artificial intelligence and implications for publishing
Ong, C., Blackbourn, H., & Migliori, G. (2023) · 2023
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OpenAI (2023) · 2023
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An important next step on our AI journey
Pichai, S. (2023) · 2023
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Summarization is (almost) dead
Pu, X., Gao, M., & Wan, X. (2023) · 2023
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OpenReviewer: A specialized large language model for generating critical scientific paper reviews
Idahl, M., & Ahmadi, Z. (2024) · 2024
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Become an IEEE reviewer
IEEE (2024) · 2024
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Generative AI in higher education: A global perspective of institutional adoption policies and guidelines
Jin, Y., Yan, L., Echeverria, V., Gašević, D., & Martinez-Maldonado, R. (2025) · 2024
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AgentReview: Exploring peer review dynamics with LLM agents
Jin, Y., Zhao, Q., Wang, Y., Chen, H., Zhu, K., Xiao, Y., & Wang, J. (2024) · 2024
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A survey of deep learning: From activations to transformers
Johannes, S., & Michalis, V. (2024) · 2024
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What can natural language processing do for peer review?
Kuznetsov, I., Afzal, O. M., Dercksen, K., Dycke, N., Goldberg, A., Hope, T., Hovy, D., Kummerfeld, J. K., Lauscher, A., & Leyton-Brown, K. (2024) · 2024
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The AI review lottery: Widespread AI-assisted peer reviews boost paper scores and acceptance rates
Latona, G. R., Ribeiro, M. H., Davidson, T. R., Veselovsky, V., & West, R. (2024) · 2024
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Evaluating and enhancing large language models for novelty assessment in scholarly publications
Lin, E., Peng, Z., & Fang, Y. (2024) · 2024
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ReviewerGPT? An exploratory study on using large language models for paper reviewing
Liu, R., & Shah, N. B. (2024) · 2024
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Llama Team (2024) · 2024
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The open source advantage in large language models (LLMs)
Manchanda, J., Boettcher, L., Westphalen, M., & Jasser, J. (2024) · 2024
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Guidelines for reviewers
MDPI (2024) · 2024
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The application of ChatGPT in the peer-reviewing process
Mehta, V., Mathur, A., Anjali, A. K., & Fiorillo, L. (2024) · 2024
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Policy on the use of AI tools by MIT Press authors
MIT Press (2024) · 2024
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Death of a reviewer or death of peer review integrity? The challenges of using AI tools in peer reviewing and the need to go beyond publishing policies
Mollaki, V. (2024) · 2024
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Exploring the potential of ChatGPT in the peer review process: An observational study
Saad, A., Jenko, N., Ariyaratne, S., Birch, N., Iyengar, K. P., Davies, A. M., Vaishya, R., & Botchu, R. (2024) · 2024
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Using AI in peer review and publishing
Sage (2024) · 2024
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A systematic survey of prompt engineering in large language models: Techniques and applications
Sahoo, P., Singh, A. K., Saha, S., Jain, V., Mondal, S., & Chadha, A. (2024) · 2024
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Evaluating science: A comparison of human and AI reviewers
Shcherbiak, A., Habibnia, H., Böhm, R., & Fiedler, S. (2024) · 2024
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On efficient training of large-scale deep learning models
Shen, L., Sun, Y., Yu, Z., Ding, L., Tian, X., & Tao, D. (2024) · 2024
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Editorial policies - Artificial intelligence
Springer Nature (2024) · 2024
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MAMORX: Multi-agent multi-modal scientific review generation with external knowledge
Taechoyotin, P., Wang, G., Zeng, T., Sides, B., & Acuna, D. (2024) · 2024
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Peer review as a multi-turn and long-context dialogue with role-based interactions
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Guidelines for peer reviewers
Taylor & Francis (2024) · 2024
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Can ChatGPT evaluate research quality?
Thelwall, M. (2024) · 2024
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AI-driven review systems: Evaluating LLMs in scalable and bias-aware academic reviews
Tyser, K., Segev, B., Longhitano, G., Zhang, X.-Y., Meeks, Z., Lee, J., Garg, U., Belsten, N., Shporer, A., & Udell, M. (2024) · 2024
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DeepNet: Scaling Transformers to 1,000 layers
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The other reviewer: RoboReviewer
Weber, R. (2024) · 2024
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A comparative study on reasoning patterns of OpenAI’s o1 model
Wu, S., Peng, Z., Du, X., Zheng, T., Liu, M., Wu, J., Ma, J., Li, Y., Yang, J., & Zhou, W. (2024) · 2024
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Hallucination is inevitable: An innate limitation of large language models
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WDMoE: Wireless Distributed Mixture of Experts for Large Language Models
Xue, N., Sun, Y., Chen, Z., Tao, M., Xu, X., Qian, L., Cui, S., Zhang, W., & Zhang, P. (2024) · 2024
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A survey on multimodal large language models
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