


@ARTICLE{Austin2021-qs,
  title         = "Program Synthesis with Large Language Models",
  author        = "Austin, Jacob and Odena, Augustus and Nye, Maxwell and
                   Bosma, Maarten and Michalewski, Henryk and Dohan, David and
                   Jiang, Ellen and Cai, Carrie and Terry, Michael and Le, Quoc
                   and Sutton, Charles",
  abstract      = "This paper explores the limits of the current generation of
                   large language models for program synthesis in general
                   purpose programming languages. We evaluate a collection of
                   such models (with between 244M and 137B parameters) on two
                   new benchmarks, MBPP and MathQA-Python, in both the few-shot
                   and fine-tuning regimes. Our benchmarks are designed to
                   measure the ability of these models to synthesize short
                   Python programs from natural language descriptions. The
                   Mostly Basic Programming Problems (MBPP) dataset contains
                   974 programming tasks, designed to be solvable by
                   entry-level programmers. The MathQA-Python dataset, a Python
                   version of the MathQA benchmark, contains 23914 problems
                   that evaluate the ability of the models to synthesize code
                   from more complex text. On both datasets, we find that
                   synthesis performance scales log-linearly with model size.
                   Our largest models, even without finetuning on a code
                   dataset, can synthesize solutions to 59.6 percent of the
                   problems from MBPP using few-shot learning with a
                   well-designed prompt. Fine-tuning on a held-out portion of
                   the dataset improves performance by about 10 percentage
                   points across most model sizes. On the MathQA-Python
                   dataset, the largest fine-tuned model achieves 83.8 percent
                   accuracy. Going further, we study the model's ability to
                   engage in dialog about code, incorporating human feedback to
                   improve its solutions. We find that natural language
                   feedback from a human halves the error rate compared to the
                   model's initial prediction. Additionally, we conduct an
                   error analysis to shed light on where these models fall
                   short and what types of programs are most difficult to
                   generate. Finally, we explore the semantic grounding of
                   these models by fine-tuning them to predict the results of
                   program execution. We find that even our best models are
                   generally unable to predict the output of a program given a
                   specific input.",
  month         =  aug,
  year          =  2021,
  url           = "http://arxiv.org/abs/2108.07732",
  file          = "All Papers/A/Austin et al. 2021 - Program Synthesis with Large Language Models.pdf",
  archivePrefix = "arXiv",
  primaryClass  = "cs.PL",
  eprint        = "2108.07732"
}

@ARTICLE{Barber2019-iw,
  title         = "The limits of distribution-free conditional predictive
                   inference",
  author        = "Barber, Rina Foygel and Cand{\`e}s, Emmanuel J and Ramdas,
                   Aaditya and Tibshirani, Ryan J",
  abstract      = "We consider the problem of distribution-free predictive
                   inference, with the goal of producing predictive coverage
                   guarantees that hold conditionally rather than marginally.
                   Existing methods such as conformal prediction offer marginal
                   coverage guarantees, where predictive coverage holds on
                   average over all possible test points, but this is not
                   sufficient for many practical applications where we would
                   like to know that our predictions are valid for a given
                   individual, not merely on average over a population. On the
                   other hand, exact conditional inference guarantees are known
                   to be impossible without imposing assumptions on the
                   underlying distribution. In this work we aim to explore the
                   space in between these two, and examine what types of
                   relaxations of the conditional coverage property would
                   alleviate some of the practical concerns with marginal
                   coverage guarantees while still being possible to achieve in
                   a distribution-free setting.",
  month         =  mar,
  year          =  2019,
  url           = "http://arxiv.org/abs/1903.04684",
  file          = "All Papers/B/Barber et al. 2019 - The limits of distribution-free conditional predictive inference.pdf",
  archivePrefix = "arXiv",
  primaryClass  = "math.ST",
  eprint        = "1903.04684"
}



@ARTICLE{Koenker1978-jo,
  title     = "Regression Quantiles",
  author    = "Koenker, Roger and Bassett, Gilbert",
  abstract  = "[A simple minimization problem yielding the ordinary sample
               quantiles in the location model is shown to generalize naturally
               to the linear model generating a new class of statistics we term
               ``regression quantiles.'' The estimator which minimizes the sum
               of absolute residuals is an important special case. Some
               equivariance properties and the joint asymptotic distribution of
               regression quantiles are established. These results permit a
               natural generalization of the linear model of certain well-known
               robust estimators of location. Estimators are suggested, which
               have comparable efficiency to least squares for Gaussian linear
               models while substantially out-performing the least-squares
               estimator over a wide class of non-Gaussian error
               distributions.]",
  journal   = "Econometrica",
  publisher = "[Wiley, Econometric Society]",
  volume    =  46,
  number    =  1,
  pages     = "33--50",
  year      =  1978,
  url       = "http://www.jstor.org/stable/1913643"
}

@ARTICLE{Vaswani2017-sk,
  title     = "Attention is All you Need",
  author    = "Vaswani, Ashish and Shazeer, Noam M and Parmar, Niki and
               Uszkoreit, Jakob and Jones, Llion and Gomez, Aidan N and Kaiser,
               Lukasz and Polosukhin, Illia",
  abstract  = "The dominant sequence transduction models are based on complex
               recurrent or convolutional neural networks in an encoder-decoder
               configuration. The best performing models also connect the
               encoder and decoder through an attention mechanism. We propose a
               new simple network architecture, the Transformer, based solely
               on attention mechanisms, dispensing with recurrence and
               convolutions entirely. Experiments on two machine translation
               tasks show these models to be superior in quality while being
               more parallelizable and requiring significantly less time to
               train. Our model achieves 28.4 BLEU on the WMT 2014
               English-to-German translation task, improving over the existing
               best results, including ensembles by over 2 BLEU. On the WMT
               2014 English-to-French translation task, our model establishes a
               new single-model state-of-the-art BLEU score of 41.8 after
               training for 3.5 days on eight GPUs, a small fraction of the
               training costs of the best models from the literature. We show
               that the Transformer generalizes well to other tasks by applying
               it successfully to English constituency parsing both with large
               and limited training data.",
  journal   = "Adv. Neural Inf. Process. Syst.",
  publisher = "proceedings.neurips.cc",
  pages     = "5998--6008",
  month     =  jun,
  year      =  2017,
  url       = "https://proceedings.neurips.cc/paper/7181-attention-is-all",
  file      = "All Papers/V/Vaswani et al. 2017 - Attention is All you Need.pdf"
}
@article{li2023textbooks,
  title={Textbooks are all you need ii: phi-1.5 technical report},
  author={Li, Yuanzhi and Bubeck, S{\'e}bastien and Eldan, Ronen and Del Giorno, Allie and Gunasekar, Suriya and Lee, Yin Tat},
  journal={arXiv preprint arXiv:2309.05463},
  year={2023}
}

@article{team2024gemma,
  title={Gemma: Open models based on gemini research and technology},
  author={Team, Gemma and Mesnard, Thomas and Hardin, Cassidy and Dadashi, Robert and Bhupatiraju, Surya and Pathak, Shreya and Sifre, Laurent and Rivi{\`e}re, Morgane and Kale, Mihir Sanjay and Love, Juliette and others},
  journal={arXiv preprint arXiv:2403.08295},
  year={2024}
}

@article{joshi2017triviaqa,
  title={Triviaqa: A large scale distantly supervised challenge dataset for reading comprehension},
  author={Joshi, Mandar and Choi, Eunsol and Weld, Daniel S and Zettlemoyer, Luke},
  journal={arXiv preprint arXiv:1705.03551},
  year={2017}
}

@article{rawte2023survey,
  title={A survey of hallucination in large foundation models},
  author={Rawte, Vipula and Sheth, Amit and Das, Amitava},
  journal={arXiv preprint arXiv:2309.05922},
  year={2023}
}

@article{yang2023diffusion,
  title={Diffusion models: A comprehensive survey of methods and applications},
  author={Yang, Ling and Zhang, Zhilong and Song, Yang and Hong, Shenda and Xu, Runsheng and Zhao, Yue and Zhang, Wentao and Cui, Bin and Yang, Ming-Hsuan},
  journal={ACM Computing Surveys},
  volume={56},
  number={4},
  pages={1--39},
  year={2023},
  publisher={ACM New York, NY, USA}
}

@article{khan2022transformers,
  title={Transformers in vision: A survey},
  author={Khan, Salman and Naseer, Muzammal and Hayat, Munawar and Zamir, Syed Waqas and Khan, Fahad Shahbaz and Shah, Mubarak},
  journal={ACM computing surveys (CSUR)},
  volume={54},
  number={10s},
  pages={1--41},
  year={2022},
  publisher={ACM New York, NY}
}
@article{brown2020language,
  title={Language models are few-shot learners},
  author={Brown, Tom and Mann, Benjamin and Ryder, Nick and Subbiah, Melanie and Kaplan, Jared D and Dhariwal, Prafulla and Neelakantan, Arvind and Shyam, Pranav and Sastry, Girish and Askell, Amanda and others},
  journal={Advances in neural information processing systems},
  volume={33},
  pages={1877--1901},
  year={2020}
}

@misc{github_copilot,
  author = {GitHub},
  title = {GitHub Copilot},
  year = {2024},
  url = {https://github.com/features/copilot},
  note = {Accessed: 2024-05-21}
}


@ARTICLE{Cobbe2021-lg,
  title         = "Training Verifiers to Solve Math Word Problems",
  author        = "Cobbe, Karl and Kosaraju, Vineet and Bavarian, Mohammad and
                   Chen, Mark and Jun, Heewoo and Kaiser, Lukasz and Plappert,
                   Matthias and Tworek, Jerry and Hilton, Jacob and Nakano,
                   Reiichiro and Hesse, Christopher and Schulman, John",
  abstract      = "State-of-the-art language models can match human performance
                   on many tasks, but they still struggle to robustly perform
                   multi-step mathematical reasoning. To diagnose the failures
                   of current models and support research, we introduce GSM8K,
                   a dataset of 8.5K high quality linguistically diverse grade
                   school math word problems. We find that even the largest
                   transformer models fail to achieve high test performance,
                   despite the conceptual simplicity of this problem
                   distribution. To increase performance, we propose training
                   verifiers to judge the correctness of model completions. At
                   test time, we generate many candidate solutions and select
                   the one ranked highest by the verifier. We demonstrate that
                   verification significantly improves performance on GSM8K,
                   and we provide strong empirical evidence that verification
                   scales more effectively with increased data than a
                   finetuning baseline.",
  month         =  oct,
  year          =  2021,
  url           = "http://arxiv.org/abs/2110.14168",
  file          = "All Papers/C/Cobbe et al. 2021 - Training Verifiers to Solve Math Word Problems.pdf",
  archivePrefix = "arXiv",
  primaryClass  = "cs.LG",
  eprint        = "2110.14168"
}


@ARTICLE{Gibbs2023-ax,
  title         = "Conformal Prediction With Conditional Guarantees",
  author        = "Gibbs, Isaac and Cherian, John J and Cand{\`e}s, Emmanuel J",
  abstract      = "We consider the problem of constructing distribution-free
                   prediction sets with finite-sample conditional guarantees.
                   Prior work has shown that it is impossible to provide exact
                   conditional coverage universally in finite samples. Thus,
                   most popular methods only provide marginal coverage over the
                   covariates. This paper bridges this gap by defining a
                   spectrum of problems that interpolate between marginal and
                   conditional validity. We motivate these problems by
                   reformulating conditional coverage as coverage over a class
                   of covariate shifts. When the target class of shifts is
                   finite dimensional, we show how to simultaneously obtain
                   exact finite sample coverage over all possible shifts. For
                   example, given a collection of protected subgroups, our
                   algorithm outputs intervals with exact coverage over each
                   group. For more flexible, infinite dimensional classes where
                   exact coverage is impossible, we provide a simple procedure
                   for quantifying the gap between the coverage of our
                   algorithm and the target level. Moreover, by tuning a single
                   hyperparameter, we allow the practitioner to control the
                   size of this gap across shifts of interest. Our methods can
                   be easily incorporated into existing split conformal
                   inference pipelines, and thus can be used to quantify the
                   uncertainty of modern black-box algorithms without
                   distributional assumptions.",
  month         =  may,
  year          =  2023,
  url           = "http://arxiv.org/abs/2305.12616",
  file          = "All Papers/G/Gibbs et al. 2023 - Conformal Prediction With Conditional Guarantees.pdf",
  archivePrefix = "arXiv",
  primaryClass  = "stat.ME",
  eprint        = "2305.12616"
}

@article{lei2014distribution,
  title={Distribution-free prediction bands for non-parametric regression},
  author={Lei, Jing and Wasserman, Larry},
  journal={Journal of the Royal Statistical Society Series B: Statistical Methodology},
  volume={76},
  number={1},
  pages={71--96},
  year={2014},
  publisher={Oxford University Press}
}

@inproceedings{Quach2023-mq,
  title={Conformal Language Modeling},
  author={Quach, Victor and Fisch, Adam and Schuster, Tal and Yala, Adam and Sohn, Jae Ho and Jaakkola, Tommi S and Barzilay, Regina},
  booktitle={The Twelfth International Conference on Learning Representations},
    year=2024
}

@BOOK{Vovk2005-cp,
  title     = "Algorithmic Learning in a Random World",
  author    = "Vovk, Vladimir and Gammerman, Alexander and Shafer, Glenn",
  abstract  = "This book is about conformal prediction, an approach to
               prediction that originated in machine learning in the late
               1990s. The main feature of conformal prediction is the
               principled …",
  publisher = "Springer International Publishing",
  year      =  2005,
  url       = "https://link.springer.com/book/10.1007/978-3-031-06649-8"
}


@article{jiang2021can,
  title={How can we know when language models know? on the calibration of language models for question answering},
  author={Jiang, Zhengbao and Araki, Jun and Ding, Haibo and Neubig, Graham},
  journal={Transactions of the Association for Computational Linguistics},
  volume={9},
  pages={962--977},
  year={2021},
  publisher={MIT Press One Rogers Street, Cambridge, MA 02142-1209, USA journals-info~…}
}

@article{desai2020calibration,
  title={Calibration of pre-trained transformers},
  author={Desai, Shrey and Durrett, Greg},
  journal={arXiv preprint arXiv:2003.07892},
  year={2020}
}

@article{lin2023generating,
  title={Generating with confidence: Uncertainty quantification for black-box large language models},
  author={Lin, Zhen and Trivedi, Shubhendu and Sun, Jimeng},
  journal={arXiv preprint arXiv:2305.19187},
  year={2023}
}

@article{christiano2017deep,
  title={Deep reinforcement learning from human preferences},
  author={Christiano, Paul F and Leike, Jan and Brown, Tom and Martic, Miljan and Legg, Shane and Amodei, Dario},
  journal={Advances in neural information processing systems},
  volume={30},
  year={2017}
}

@article{achiam2023gpt,
  title={Gpt-4 technical report},
  author={Achiam, Josh and Adler, Steven and Agarwal, Sandhini and Ahmad, Lama and Akkaya, Ilge and Aleman, Florencia Leoni and Almeida, Diogo and Altenschmidt, Janko and Altman, Sam and Anadkat, Shyamal and others},
  journal={arXiv preprint arXiv:2303.08774},
  year={2023}
}

@article{kuhn2023semantic,
  title={Semantic uncertainty: Linguistic invariances for uncertainty estimation in natural language generation},
  author={Kuhn, Lorenz and Gal, Yarin and Farquhar, Sebastian},
  journal={arXiv preprint arXiv:2302.09664},
  year={2023}
}

@article{holtzman2021surface,
  title={Surface form competition: Why the highest probability answer isn't always right},
  author={Holtzman, Ari and West, Peter and Shwartz, Vered and Choi, Yejin and Zettlemoyer, Luke},
  journal={arXiv preprint arXiv:2104.08315},
  year={2021}
}

@article{wang2022probabilistic,
  title={Probabilistic conformal prediction using conditional random samples},
  author={Wang, Zhendong and Gao, Ruijiang and Yin, Mingzhang and Zhou, Mingyuan and Blei, David M},
  journal={arXiv preprint arXiv:2206.06584},
  year={2022}
}

@article{kumar2023conformal,
  title={Conformal prediction with large language models for multi-choice question answering},
  author={Kumar, Bhawesh and Lu, Charlie and Gupta, Gauri and Palepu, Anil and Bellamy, David and Raskar, Ramesh and Beam, Andrew},
  journal={arXiv preprint arXiv:2305.18404},
  year={2023}
}

@article{rouzrokh2024conflare,
  title={CONFLARE: CONFormal LArge language model REtrieval},
  author={Rouzrokh, Pouria and Faghani, Shahriar and Gamble, Cooper U and Shariatnia, Moein and Erickson, Bradley J},
  journal={arXiv preprint arXiv:2404.04287},
  year={2024}
}

@article{romano2019conformalized,
  title={Conformalized quantile regression},
  author={Romano, Yaniv and Patterson, Evan and Candes, Emmanuel},
  journal={Advances in neural information processing systems},
  volume={32},
  year={2019}
}

@article{romano2020classification,
  title={Classification with valid and adaptive coverage},
  author={Romano, Yaniv and Sesia, Matteo and Candes, Emmanuel},
  journal={Advances in Neural Information Processing Systems},
  volume={33},
  pages={3581--3591},
  year={2020}
}

@misc{angelopoulos-sets,
	title={Uncertainty Sets for Image Classifiers using Conformal Prediction},
	author={Angelopoulos, Anastasios Nikolas and Bates, Stephen and Malik, Jitendra and Jordan, Michael I.},
	url={https://arxiv.org/abs/2009.14193},
	journal={arXiv:2009.14193},
	year={2020}
}

@article{jung2022batch,
  title={Batch multivalid conformal prediction},
  author={Jung, Christopher and Noarov, Georgy and Ramalingam, Ramya and Roth, Aaron},
  journal={arXiv preprint arXiv:2209.15145},
  year={2022}
}

@article{sadinle2019least,
  title={Least ambiguous set-valued classifiers with bounded error levels},
  author={Sadinle, Mauricio and Lei, Jing and Wasserman, Larry},
  journal={Journal of the American Statistical Association},
  volume={114},
  number={525},
  pages={223--234},
  year={2019},
  publisher={Taylor \& Francis}
}

@article{angelopoulos2022conformal,
  title={Conformal risk control},
  author={Angelopoulos, Anastasios N and Bates, Stephen and Fisch, Adam and Lei, Lihua and Schuster, Tal},
  journal={arXiv preprint arXiv:2208.02814},
  year={2022}
}

@article{angelopoulos2021learn,
  title={Learn then test: Calibrating predictive algorithms to achieve risk control},
  author={Angelopoulos, Anastasios N and Bates, Stephen and Cand{\`e}s, Emmanuel J and Jordan, Michael I and Lei, Lihua},
  journal={arXiv preprint arXiv:2110.01052},
  year={2021}
}

@article{su2024api,
  title={Api is enough: Conformal prediction for large language models without logit-access},
  author={Su, Jiayuan and Luo, Jing and Wang, Hongwei and Cheng, Lu},
  journal={arXiv preprint arXiv:2403.01216},
  year={2024}
}

@article{wang2024conu,
  title={ConU: Conformal Uncertainty in Large Language Models with Correctness Coverage Guarantees},
  author={Wang, Zhiyuan and Duan, Jinhao and Cheng, Lu and Zhang, Yue and Wang, Qingni and Shen, Hengtao and Zhu, Xiaofeng and Shi, Xiaoshuang and Xu, Kaidi},
  journal={arXiv preprint arXiv:2407.00499},
  year={2024}
}

@inproceedings{lai2023ds,
  title={DS-1000: A natural and reliable benchmark for data science code generation},
  author={Lai, Yuhang and Li, Chengxi and Wang, Yiming and Zhang, Tianyi and Zhong, Ruiqi and Zettlemoyer, Luke and Yih, Wen-tau and Fried, Daniel and Wang, Sida and Yu, Tao},
  booktitle={International Conference on Machine Learning},
  pages={18319--18345},
  year={2023},
  organization={PMLR}
}

@article{hendrycks2021measuring,
  title={Measuring mathematical problem solving with the math dataset},
  author={Hendrycks, Dan and Burns, Collin and Kadavath, Saurav and Arora, Akul and Basart, Steven and Tang, Eric and Song, Dawn and Steinhardt, Jacob},
  journal={arXiv preprint arXiv:2103.03874},
  year={2021}
}

@book{bakir2007predicting,
  title={Predicting structured data},
  author={BakIr, G{\"o}khan},
  year={2007},
  publisher={MIT press}
}

@article{holtzman2019curious,
  title={The curious case of neural text degeneration},
  author={Holtzman, Ari and Buys, Jan and Du, Li and Forbes, Maxwell and Choi, Yejin},
  journal={arXiv preprint arXiv:1904.09751},
  year={2019}
}

@article{asare2023github,
  title={Is github’s copilot as bad as humans at introducing vulnerabilities in code?},
  author={Asare, Owura and Nagappan, Meiyappan and Asokan, N},
  journal={Empirical Software Engineering},
  volume={28},
  number={6},
  pages={129},
  year={2023},
  publisher={Springer}
}

@article{huang2023look,
  title={Look before you leap: An exploratory study of uncertainty measurement for large language models},
  author={Huang, Yuheng and Song, Jiayang and Wang, Zhijie and Zhao, Shengming and Chen, Huaming and Juefei-Xu, Felix and Ma, Lei},
  journal={arXiv preprint arXiv:2307.10236},
  year={2023}
}



@article{foygel2021limits,
  title={The limits of distribution-free conditional predictive inference},
  author={Foygel Barber, Rina and Candes, Emmanuel J and Ramdas, Aaditya and Tibshirani, Ryan J},
  journal={Information and Inference: A Journal of the IMA},
  volume={10},
  number={2},
  pages={455--482},
  year={2021},
  publisher={Oxford University Press}
}

@article{angelopoulos2020uncertainty,
  title={Uncertainty sets for image classifiers using conformal prediction},
  author={Angelopoulos, Anastasios and Bates, Stephen and Malik, Jitendra and Jordan, Michael I},
  journal={arXiv preprint arXiv:2009.14193},
  year={2020}
}


@article{huang2024uncertainty,
  title={Uncertainty quantification over graph with conformalized graph neural networks},
  author={Huang, Kexin and Jin, Ying and Candes, Emmanuel and Leskovec, Jure},
  journal={Advances in Neural Information Processing Systems},
  volume={36},
  year={2024}
}

@article{tibshirani2019conformal,
  title={Conformal prediction under covariate shift},
  author={Tibshirani, Ryan J and Foygel Barber, Rina and Candes, Emmanuel and Ramdas, Aaditya},
  journal={Advances in neural information processing systems},
  volume={32},
  year={2019}
}

@article{bates2021distribution,
  title={Distribution-free, risk-controlling prediction sets},
  author={Bates, Stephen and Angelopoulos, Anastasios and Lei, Lihua and Malik, Jitendra and Jordan, Michael},
  journal={Journal of the ACM (JACM)},
  volume={68},
  number={6},
  pages={1--34},
  year={2021},
  publisher={ACM New York, NY}
}

@inproceedings{papadopoulos2002inductive,
  title={Inductive confidence machines for regression},
  author={Papadopoulos, Harris and Proedrou, Kostas and Vovk, Volodya and Gammerman, Alex},
  booktitle={Machine learning: ECML 2002: 13th European conference on machine learning Helsinki, Finland, August 19--23, 2002 proceedings 13},
  pages={345--356},
  year={2002},
  organization={Springer}
}

@article{malinin2020uncertainty,
  title={Uncertainty estimation in autoregressive structured prediction},
  author={Malinin, Andrey and Gales, Mark},
  journal={arXiv preprint arXiv:2002.07650},
  year={2020}
}

@article{farquhar2024detecting,
  title={Detecting hallucinations in large language models using semantic entropy},
  author={Farquhar, Sebastian and Kossen, Jannik and Kuhn, Lorenz and Gal, Yarin},
  journal={Nature},
  volume={630},
  number={8017},
  pages={625--630},
  year={2024},
  publisher={Nature Publishing Group UK London}
}

@article{ryabinin2021scaling,
  title={Scaling ensemble distribution distillation to many classes with proxy targets},
  author={Ryabinin, Max and Malinin, Andrey and Gales, Mark},
  journal={Advances in Neural Information Processing Systems},
  volume={34},
  pages={6023--6035},
  year={2021}
}

@article{mohri2024language,
  title={Language models with conformal factuality guarantees},
  author={Mohri, Christopher and Hashimoto, Tatsunori},
  journal={arXiv preprint arXiv:2402.10978},
  year={2024}
}

@article{cherian2024large,
  title={Large language model validity via enhanced conformal prediction methods},
  author={Cherian, John J and Gibbs, Isaac and Cand{\`e}s, Emmanuel J},
  journal={arXiv preprint arXiv:2406.09714},
  year={2024}
}
@inproceedings{li2024traq,
  title={TRAQ: Trustworthy Retrieval Augmented Question Answering via Conformal Prediction},
  author={Li, Shuo and Park, Sangdon and Lee, Insup and Bastani, Osbert},
  booktitle={Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)},
  pages={3799--3821},
  year={2024}
}

@inproceedings{zhang2021less,
  title={Less is more: Rejecting unreliable reviews for product question answering},
  author={Zhang, Shiwei and Zhang, Xiuzhen and Lau, Jey Han and Chan, Jeffrey and Paris, Cecile},
  booktitle={Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2020, Ghent, Belgium, September 14--18, 2020, Proceedings, Part III},
  pages={567--583},
  year={2021},
  organization={Springer}
}

@article{kadavath2022language,
  title={Language models (mostly) know what they know},
  author={Kadavath, Saurav and Conerly, Tom and Askell, Amanda and Henighan, Tom and Drain, Dawn and Perez, Ethan and Schiefer, Nicholas and Hatfield-Dodds, Zac and DasSarma, Nova and Tran-Johnson, Eli and others},
  journal={arXiv preprint arXiv:2207.05221},
  year={2022}
}

@article{gupta2022nested,
  title={Nested conformal prediction and quantile out-of-bag ensemble methods},
  author={Gupta, Chirag and Kuchibhotla, Arun K and Ramdas, Aaditya},
  journal={Pattern Recognition},
  volume={127},
  pages={108496},
  year={2022},
  publisher={Elsevier}
}

@article{liang2024conformal,
  title={Conformal prediction after efficiency-oriented model selection},
  author={Liang, Ruiting and Zhu, Wanrong and Barber, Rina Foygel},
  journal={arXiv preprint arXiv:2408.07066},
  year={2024}
}

@inproceedings{vovk2012conditional,
  title={Conditional validity of inductive conformal predictors},
  author={Vovk, Vladimir},
  booktitle={Asian conference on machine learning},
  pages={475--490},
  year={2012},
  organization={PMLR}
}

@article{angelopoulos2024theoretical,
  title={Theoretical foundations of conformal prediction},
  author={Angelopoulos, Anastasios N and Barber, Rina Foygel and Bates, Stephen},
  journal={arXiv preprint arXiv:2411.11824},
  year={2024}
}