Fetching the paper…
Reading the bibliography…
As Large Language Models become more ubiquitous across domains, it becomes important to examine their inherent limitations critically.
M. O. Rabin, “Probabilistic automata,” Information and Control
1963
Earlier work this paper cites.
New York: Signet Books, 1966
M. McLuhan, Understanding media: the Extensions of Man · 1966
Earlier work this paper cites.
Y. Choueka, “Theories of automata on w-tapes: A simplified approach,” Journal of Computer and System Sciences
1974
Earlier work this paper cites.
C. Smorynski, “The incompleteness theorems,” in Handbook of mathematical logic
1977
Earlier work this paper cites.
D. Wolpert and W. G. Macready, “No free lunch theorems for optimization,” IEEE Transactions on Evolutionary Computation
1997
Earlier work this paper cites.
India Private Limited: Cengage, third ed., 2006
M. Sipser, Introduction to the Theory of Computation · 2006
Earlier work this paper cites.
C. D. Manning, P. Raghavan, and H. Schütze, “Introduction to information retrieval. cambridge university press,” 2008
2008
Earlier work this paper cites.
Cambridge University Press, 2009
J. Pearl, CAUSALITY Models, Reasoning and Inference · 2009
Earlier work this paper cites.
R. I. Soare, “Turing oracle machines, online computing, and three displacements in computability theory,” Annals of Pure and Applied Logic
2009
Earlier work this paper cites.
H. Gimbert and Y. Oualhadj, “Probabilistic automata on finite words: Decidable and undecidable problems,” Ffhal-00456538v1f
2010
Earlier work this paper cites.
Association for Computational Linguistics, 2016
Z. Tu et al · 2016
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,” tech. rep., Preprint, arXiv. Jun. 12, 2017
2017
Earlier work this paper cites.
E. Radiya-Dixit and X. Wang, “How fine can fine-tuning be? learning efficient language models,” PMLR
2020
Earlier work this paper cites.
N. Ståhl, G. Falkman, A. Karlsson, and G. Mathiason, “Evaluation of uncertainty quantification in deep learning,” Communications in computer and information science
2020
Cited alongside, same era.
T. Shi, Y. Keneshloo, and N. Ramakrishnan, “Chandan reddy,” Neural Abstractive Text Summarization with Sequence-to-Sequence Models. ACM/IMS Transactions on Data Science
2021
Cited alongside, same era.
K. W. Church, Z. Chen, and Y. Ma, “Emerging trends: A gentle introduction to fine-tuning,” Natural Language Engineering
2021
Cited alongside, same era.
E. Hu, Y. Shen, P. Wallis, Z. Allen-Zhu, Y. Li, S. Wang, L. Wang, and W. Chen, “Lora: Low-rank adaptation of large language models.” Preprint, arXiv, October 2021
2021
Cited alongside, same era.
M. Abdar et al
2021
Cited alongside, same era.
A. Atabey and R. Scarff, “The fairness principle: A tool to protect childrens rights in their interaction with emotional ai in educational settings,” Global Privacy Law Review
2023
Later among the works it cites.
G. Buarque, “Artificial intelligence and algorithmic discrimination: a reflection on risk and vulnerability in childhood,” Brazilian Journal of Law, Technology and Innovation
2023
Later among the works it cites.
M. Hicks, J. Humphries, and J. Slater, “Chatgpt is bullshit,” Ethics and Information Technology
2024
Closest in time.
” The Gradient, Mar. 28
“Mamba, “Explained,” 2024 · 2024
Closest in time.
Accessed: Aug. 12
H. Zheng et al · 2024
Closest in time.
Preprint, arXiv.. Accessed: Aug. 12
N. Houlsby, A. Giurgiu, S. Jastrzebski, B. Morrone, Q. de Laroussilhe, A. Gesmundo, M. Attariyan, and S. Gelly, “Parameter-efficient transfer learning for nlp,” 2024 · 2024
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
L. Huang, W. Yu, W. Ma, W. Zhong, Z. Feng, H. Wang, Q. Chen, W. Peng, X. Feng, B. Qin, and T. Liu, “A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions.” Preprint, 2023
2023
Cited alongside, same era.
N. Ding, Y. Qin, G. Yang, F. Wei, Z. Yang, Y. Su, S. Hu, Y. Chen, C.-M. Chan, W. Chen, J. Yi, W. Zhao, X. Wang, Z. Liu, H.-T. Zheng, J. Chen, Y. Liu, J. Tang, J. Li, and M. Sun, “Parameter-efficient fine-tuning of large-scale pre-trained language models. nature machine intelligence,” March 2023
2023
Cited alongside, same era.
J. Huang and K. C.-C. Chang, “Towards reasoning in large language models: A survey.” Preprint, arXiv, 2023
2023
Cited alongside, same era.
The Lancet Digital Health, 2023
Editorial, ChatGPT: friend or foe? · 2023
Cited alongside, same era.
T. Dave, S. A. Athaluri, and S. Singh, “Chatgpt in medicine: an overview of its applications, advantages, limitations, future prospects, and ethical considerations,” Frontiers in Artificial Intelligence
2023
Cited alongside, same era.
O. Oviedo-Trespalacios, A. E. Peden, T. Cole-Hunter, A. Costantini, M. Haghani, J. E. Rod, S. Kelly, H. Torkamaan, A. Tariq, J. D. A. Newton, T. Gallagher, and S. Steinert, “The risks of using chatgpt to obtain common safety-related information and advice,” Safety Science
2023
Cited alongside, same era.
C. K. Lo, “What is the impact of chatgpt on education? a rapid review of the literature,” Education Sciences
2023
Cited alongside, same era.
Closest in time.
Accessed: Aug. 12
E. Ben-Zaken, S. Ravfogel, and Y. Goldberg, “Bitfit: Simple parameter-efficient fine-tuning for transformer-based masked language-models,” 2024 · 2024
Closest in time.
Q. Lyu, M. Apidianaki, and C. Callison-Burch, “Towards faithful model explanation in nlp: A survey,” Computational Linguistics
2024
Closest in time.
G. Rote, “Probabilistic finite automaton emptiness is undecidable.” Preprint, [cs.FL], May 2024
2024
Closest in time.
J. Haltaufderheide and R. Ranisch, “The ethics of chatgpt in medicine and healthcare: a systematic review on large language models (llms). npj digital medicine,” 2024
2024
Closest in time.
M. S. Khan and H. Umer, “Chatgpt in finance: Applications, challenges, and solutions,” Heliyon
2024
Closest in time.
M. A. Beltran, M. I. R. Mondragon, and S. H. Han, “Comparative analysis of generative ai risks in the public sector,” in Proceedings of the 25th Annual International Conference on Digital Government Research (dg
2024
Closest in time.
AI & Soc, 2024
T. Cantens, How will the state think with ChatGPT? The challenges of generative artificial intelligence for public administrations · 2024
Closest in time.