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Large language models (LLMs) are increasingly applied to multi-modal data analysis -- not necessarily because they offer the most precise answers, but because they provide fluent, flexible interfaces for interpreting complex inputs.
A survey on privacy-preserving machine learning with fully homomorphic encryption
L. B. Pulido-Gaytan, A. Tchernykh, J. M. Cortés-Mendoza, M. Babenko, and G. Radchenko · 2020
Earlier work this paper cites.
On the dangers of stochastic parrots: Can language models be too big?
E. M. Bender, T. Gebru, A. McMillan-Major, and S. Shmitchell · 2021
Earlier work this paper cites.
Privacy-preserving machine learning: Methods, challenges and directions
R. Xu, N. Baracaldo, and J. Joshi · 2021
Earlier work this paper cites.
Measuring and narrowing the compositionality gap in language models
O. Press, M. Zhang, S. Min, L. Schmidt, N. A. Smith, and M. Lewis · 2022
Earlier work this paper cites.
Synthesizing privacy preserving entity resolution datasets
X. Qin, C. Chai, N. Tang, J. Li, Y. Luo, G. Li, and Y. Zhu · 2022
Earlier work this paper cites.
J. Achiam, S. Adler, S. Agarwal, L. Ahmad, I. Akkaya, F. L. Aleman, D. Almeida, J. Altenschmidt, S. Altman, S. Anadkat, et al · 2023
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Pal: Program-aided language models
L. Gao, A. Madaan, S. Zhou, U. Alon, P. Liu, Y. Yang, J. Callan, and G. Neubig · 2023
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Survey of hallucination in natural language generation
Z. Ji, N. Lee, R. Frieske, T. Yu, D. Su, Y. Xu, E. Ishii, Y. J. Bang, A. Madotto, and P. Fung · 2023
Cited alongside, same era.
Lost in the middle: How language models use long contexts
N. F. Liu, K. Lin, J. Hewitt, A. Paranjape, M. Bevilacqua, F. Petroni, and P. Liang · 2023
Cited alongside, same era.
B. Peng, M. Galley, P. He, H. Cheng, Y. Xie, Y. Hu, Q. Huang, L. Liden, Z. Yu, W. Chen, et al · 2023
Cited alongside, same era.
Explainable and interpretable multimodal large language models: A comprehensive survey
Y. Dang, K. Huang, J. Huo, Y. Yan, S. Huang, D. Liu, M. Gao, J. Zhang, C. Qian, K. Wang, et al · 2024
Cited alongside, same era.
Multimodal foundation models: From specialists to general-purpose assistants
Symphony: Towards trustworthy question answering and verification using RAG over multimodal data lakes
N. Tang, C. Yang, Z. Zhang, Y. Luo, J. Fan, L. Cao, S. Madden, and A. Y. Halevy · 2024
Later among the works it cites.
J. Wang, H. Jiang, Y. Liu, C. Ma, X. Zhang, Y. Pan, M. Liu, P. Gu, S. Xia, W. Li, et al · 2024
Later among the works it cites.
Model compression and efficient inference for large language models: A survey
W. Wang, W. Chen, Y. Luo, Y. Long, Z. Lin, L. Zhang, B. Lin, D. Cai, and X. He · 2024
Later among the works it cites.
A survey on efficient inference for large language models
Z. Zhou, X. Ning, K. Hong, T. Fu, J. Xu, S. Li, Y. Lou, L. Wang, Z. Yuan, X. Li, et al · 2024
Later among the works it cites.
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C. Li, Z. Gan, Z. Yang, J. Yang, L. Li, L. Wang, J. Gao, et al · 2024
Cited alongside, same era.
Sniffer: Multimodal large language model for explainable out-of-context misinformation detection
P. Qi, Z. Yan, W. Hsu, and M. L. Lee · 2024
Cited alongside, same era.
Verifai: Verified generative AI
N. Tang, C. Yang, J. Fan, L. Cao, Y. Luo, and A. Y. Halevy · 2024
Cited alongside, same era.
Gsm-symbolic: Understanding the limitations of mathematical reasoning in large language models
S. I. Mirzadeh, K. Alizadeh, H. Shahrokhi, O. Tuzel, S. Bengio, and M. Farajtabar
Cited in the paper.
Are large language models good statisticians?
Y. Zhu, S. Du, B. Li, Y. Luo, and N. Tang
Cited in the paper.
L. Huang, W. Yu, W. Ma, W. Zhong, Z. Feng, H. Wang, Q. Chen, W. Peng, X. Feng, B. Qin, et al · 2025
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S. Shrestha, M. Kim, and K. Ross · 2025
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