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Foundation models, specifically Large Language Models (LLMs), have lately gained wide-spread attention and adoption.
Fine-tuning language models from human preferences
Ziegler, D. M.; Stiennon, N.; Wu, J.; Brown, T. B.; Radford, A.; Amodei, D.; Christiano, P.; and Irving, G. 2019 · 1909
Earlier work this paper cites.
DeBERTa: Decoding-enhanced BERT with Disentangled Attention
He, P.; Liu, X.; Gao, J.; and Chen, W. 2021 · 2006
Earlier work this paper cites.
A baseline for detecting misclassified and out-of-distribution examples in neural networks
Hendrycks, D.; and Gimpel, K. 2016 · 2016
Earlier work this paper cites.
Deep reinforcement learning from human preferences
Christiano, P. F.; Leike, J.; Brown, T.; Martic, M.; Legg, S.; and Amodei, D. 2017 · 2017
Earlier work this paper cites.
On calibration of modern neural networks
Guo, C.; Pleiss, G.; Sun, Y.; and Weinberger, K. Q. 2017 · 2017
Earlier work this paper cites.
Enhancing the reliability of out-of-distribution image detection in neural networks
Liang, S.; Li, Y.; and Srikant, R. 2017 · 2017
Earlier work this paper cites.
A simple unified framework for detecting out-of-distribution samples and adversarial attacks
Lee, K.; Lee, K.; Lee, H.; and Shin, J. 2018 · 2018
Earlier work this paper cites.
Beyond temperature scaling: Obtaining well-calibrated multi-class probabilities with dirichlet calibration
Kull, M.; Perello Nieto, M.; Kängsepp, M.; Silva Filho, T.; Song, H.; and Flach, P. 2019 · 2019
Earlier work this paper cites.
Can you trust your model's uncertainty? Evaluating predictive uncertainty under dataset shift
Ovadia, Y.; Fertig, E.; Ren, J.; Nado, Z.; Sculley, D.; Nowozin, S.; Dillon, J.; Lakshminarayanan, B.; and Snoek, J. 2019 · 2019
Earlier work this paper cites.
Accurate Layerwise Interpretable Competence Estimation
Rajendran, V.; and LeVine, W. 2019 · 2019
Cited alongside, same era.
Generalized odin: Detecting out-of-distribution image without learning from out-of-distribution data
Hsu, Y.-C.; Shen, Y.; Jin, H.; and Kira, Z. 2020 · 2020
Cited alongside, same era.
Energy-based out-of-distribution detection
Liu, W.; Wang, X.; Owens, J.; and Li, Y. 2020 · 2020
Cited alongside, same era.
Learning to summarize from human feedback
Stiennon, N.; Ouyang, L.; Wu, J.; Ziegler, D. M.; Lowe, R.; Voss, C.; Radford, A.; Amodei, D.; and Christiano, P. 2020 · 2020
Cited alongside, same era.
OPUS-MT — Building open translation services for the World
Tiedemann, J.; and Thottingal, S. 2020 · 2020
Cited alongside, same era.
On the importance of gradients for detecting distributional shifts in the wild
Huang, R.; Geng, A.; and Li, Y. 2021 · 2021
Training ood detectors in their natural habitats
Katz-Samuels, J.; Nakhleh, J. B.; Nowak, R.; and Li, Y. 2022 · 2022
Later among the works it cites.
Out-of-distribution Detection with Deep Nearest Neighbors
Sun, Y.; Ming, Y.; Zhu, X.; and Li, Y. 2022 · 2022
Later among the works it cites.
Generalization Analogies: A Testbed for Generalizing AI Oversight to Hard-To-Measure Domains
Clymer, J.; Baker, G.; Subramani, R.; and Wang, S. 2023 · 2023
Closest in time.
OpenAssistant Conversations - Democratizing Large Language Model Alignment
Köpf, A.; Kilcher, Y.; von Rütte, D.; Anagnostidis, S.; Tam, Z.; Stevens, K.; Barhoum, A.; Duc, N. M.; Stanley, O.; Nagyfi, R.; ES, S.; Suri, S.; Glushkov, D.; Dantuluri, A.; Maguire, A.; Schuhmann, C.; Nguyen, H.; and Mattick, A. 2023 · 2023
Closest in time.
Enabling Calibration In The Zero-Shot Inference of Large Vision-Language Models
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Cited alongside, same era.
React: Out-of-distribution detection with rectified activations
Sun, Y.; Guo, C.; and Li, Y. 2021 · 2021
Cited alongside, same era.
On the Effectiveness of Sparsification for Detecting the Deep Unknowns
Sun, Y.; and Li, Y. 2021 · 2021
Cited alongside, same era.
Extremely Simple Activation Shaping for Out-of-Distribution Detection
Djurisic, A.; Bozanic, N.; Ashok, A.; and Liu, R. 2022 · 2022
Cited alongside, same era.
LeVine, W.; Pikus, B.; Raj, P.; and Gil, F. A. 2023 · 2023
Closest in time.
Lightman, H.; Kosaraju, V.; Burda, Y.; Edwards, H.; Baker, B.; Lee, T.; Leike, J.; Schulman, J.; Sutskever, I.; and Cobbe, K. 2023 · 2023
Closest in time.
How Good Are Large Language Models at Out-of-Distribution Detection?
Liu, B.; Zhan, L.; Lu, Z.; Feng, Y.; Xue, L.; and Wu, X.-M. 2023 · 2023
Closest in time.
Out-of-Distribution Detection & Applications With Ablated Learned Temperature Energy
LeVine, W.; Pikus, B.; Phillips, J.; Norman, B.; Gil, F. A.; and Hendryx, S. 2024 · 2024
Closest in time.