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As large language models continue to be widely developed, robust uncertainty quantification techniques will become crucial for their safe deployment in high-stakes scenarios.
The concept of exchangeability and its applications
Bernardo, J. M · 1996
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Learning by transduction, 2013
Gammerman, A., Vovk, V., and Vapnik, V · 2013
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Least ambiguous set-valued classifiers with bounded error levels
Sadinle, M., Lei, J., and Wasserman, L · 2017
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Uncertainty as a form of transparency: Measuring, communicating, and using uncertainty
Bhatt, U., Antorán, J., Zhang, Y., Liao, Q. V., Sattigeri, P., Fogliato, R., Melançon, G., Krishnan, R., Stanley, J., Tickoo, O., et al · 2021
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Measuring massive multitask language understanding, 2021
Hendrycks, D., Burns, C., Basart, S., Zou, A., Mazeika, M., Song, D., and Steinhardt, J · 2021
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How can we know when language models know? on the calibration of language models for question answering
Jiang, Z., Araki, J., Ding, H., and Neubig, G · 2021
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On the utility of prediction sets in human-ai teams
Babbar, V., Bhatt, U., and Weller, A · 2022
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Language models (mostly) know what they know, 2022
Kadavath, S., Conerly, T., Askell, A., Henighan, T., Drain, D., Perez, E., Schiefer, N., Hatfield-Dodds, Z., DasSarma, N., Tran-Johnson, E., Johnston, S., El-Showk, S., Jones, A., Elhage, N., Hume, T., Chen, A., Bai, Y., Bowman, S., Fort, S., Ganguli, D., Hernandez, D., Jacobson, J., Kernion, J., Kravec, S., Lovitt, L., Ndousse, K., Olsson, C., Ringer, S., Amodei, D., Brown, T., Clark, J., Joseph, N., Mann, B., McCandlish, S., Olah, C., and Kaplan, J · 2022
Cited alongside, same era.
Towards reliable zero shot classification in self-supervised models with conformal prediction
Kumar, B., Palepu, A., Tuwani, R., and Beam, A · 2022
Cited alongside, same era.
Distribution-free federated learning with conformal predictions, 2022
Lu, C. and Kalpathy-Cramer, J · 2022
Cited alongside, same era.
Improving trustworthiness of ai disease severity rating in medical imaging with ordinal conformal prediction sets
Algorithmic Learning in a Random World
Vovk, V., Gammerman, A., and Shafer, G · 2022
Later among the works it cites.
Do large language models need sensory grounding for meaning and understanding?
LeCun, Y · 2023
Closest in time.
Llama: Open and efficient foundation language models
Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., Rodriguez, A., Joulin, A., Grave, E., and Lample, G · 2023
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Chain-of-thought prompting elicits reasoning in large language models, 2023
Wei, J., Wang, X., Schuurmans, D., Bosma, M., Ichter, B., Xia, F., Chi, E., Le, Q., and Zhou, D · 2023
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Least-to-most prompting enables complex reasoning in large language models, 2023
Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., and Chi, E · 2023
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Lu, C., Angelopoulos, A. N., and Pomerantz, S · 2022
Cited alongside, same era.
Re-examining calibration: The case of question answering
Si, C., Zhao, C., Min, S., and Boyd-Graber, J · 2022
Cited alongside, same era.
Uncertainty sets for image classifiers using conformal prediction, 2022a
Angelopoulos, A., Bates, S., Malik, J., and Jordan, M. I
Cited in the paper.
A gentle introduction to conformal prediction and distribution-free uncertainty quantification
Angelopoulos, A. N. and Bates, S
Cited in the paper.
A gentle introduction to conformal prediction and distribution-free uncertainty quantification
Angelopoulos, A. N. and Bates, S
Cited in the paper.
Learn then test: Calibrating predictive algorithms to achieve risk control, 2022b
Angelopoulos, A. N., Bates, S., Candès, E. J., Jordan, M. I., and Lei, L
Cited in the paper.
Empirical frequentist coverage of deep learning uncertainty quantification procedures
Kompa, B., Snoek, J., and Beam, A. L
Cited in the paper.
Second opinion needed: communicating uncertainty in medical machine learning
Kompa, B., Snoek, J., and Beam, A. L
Cited in the paper.