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Large language models show impressive results at predicting structured text such as code, but also commonly introduce errors and hallucinations in their output.
An optimum character recognition system using decision functions
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Automation bias in intelligent time critical decision support systems
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Minimum Bayes-risk decoding for statistical machine translation
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Algorithmic learning in a random world
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Minimum Bayes risk decoding for bleu
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Lattice minimum Bayes-risk decoding for statistical machine translation
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On the foundations of noise-free selective classification
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A risk minimization framework for extractive speech summarization
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Mining source code for structural regularities
Lozano, A., Kellens, A., Mens, K., and Arévalo, G. B · 2010
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On dual decomposition and linear programming relaxations for natural language processing
Rush, A. M., Sontag, D. A., Collins, M., and Jaakkola, T · 2010
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An improved consensus-like method for minimum Bayes risk decoding and lattice combination
Xu, H., Povey, D., Mangu, L., and Zhu, J · 2010
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Introduction to dual composition for inference
Sontag, D., Globerson, A., and Jaakkola, T · 2011
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Multiple choice learning: Learning to produce multiple structured outputs
Guzman-Rivera, A., Batra, D., and Kohli, P · 2012
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Implicitly intersecting weighted automata using dual decomposition
Paul, M. J. and Eisner, J · 2012
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A tutorial on dual decomposition and Lagrangian relaxation for inference in natural language processing
Rush, A. M. and Collins, M · 2012
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Decision diagrams and dynamic programming
Hooker, J. N · 2013
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Mining idioms from source code
Allamanis, M. and Sutton, C · 2014
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Efficiently enforcing diversity in multi-output structured prediction
Guzman-Rivera, A., Kohli, P., Batra, D., and Rutenbar, R · 2014
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A new look at reweighted message passing
Kolmogorov, V · 2014
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Submodular meets structured: Finding diverse subsets in exponentially-large structured item sets
Prasad, A., Jegelka, S., and Batra, D · 2014
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Empirical minimum Bayes risk prediction: How to extract an extra few % performance from vision models with just three more parameters
Premachandran, V., Tarlow, D., and Batra, D · 2014
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Minimum Bayes’ risk subsequence combination for machine translation
González-Rubio, J. and Casacuberta, F · 2015
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Numba: a llvm-based python jit compiler
Lam, S. K., Pitrou, A., and Seibert, S · 2015
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Dual decomposition inference for graphical models over strings
Peng, N., Cotterell, R., and Eisner, J · 2015
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Distilling an ensemble of greedy dependency parsers into one mst parser
Kuncoro, A., Ballesteros, M., Kong, L., Dyer, C., and Smith, N. A · 2016
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Imitation learning for language generation from unaligned data
Lampouras, G. and Vlachos, A · 2016
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Stochastic multiple choice learning for training diverse deep ensembles
Lee, S., Purushwalkam Shiva Prakash, S., Cogswell, M., Ranjan, V., Crandall, D., and Batra, D · 2016
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A dual ascent framework for Lagrangean decomposition of combinatorial problems
Swoboda, P., Kuske, J., and Savchynskyy, B · 2016
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Selective classification for deep neural networks
Geifman, Y. and El-Yaniv, R · 2017
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Automation bias and verification complexity: a systematic review
Lyell, D. and Coiera, E. W · 2017
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Neural lattice-to-sequence models for uncertain inputs
Sperber, M., Neubig, G., Niehues, J., and Waibel, A. H · 2017
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Lattice-based recurrent neural network encoders for neural machine translation
Su, J., Tan, Z., Xiong, D., Ji, R., Shi, X., and Liu, Y · 2017
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Deeply AggreVaTeD: Differentiable imitation learning for sequential prediction
Sun, W., Venkatraman, A., Gordon, G. J., Boots, B., and Bagnell, J. A · 2017
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Accurate and diverse sampling of sequences based on a “best of many” sample objective
Bhattacharyya, A., Schiele, B., and Fritz, M · 2018
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On the relative succinctness of sentential decision diagrams
Bollig, B. and Buttkus, M · 2018
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A stable and effective learning strategy for trainable greedy decoding
Chen, Y., Li, V. O., Cho, K., and Bowman, S. R · 2018
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Confidence modeling for neural semantic parsing
Dong, L., Quirk, C., and Lapata, M · 2018
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Diversenet: When one right answer is not enough
Learning to complete code with sketches
Guo, D., Svyatkovskiy, A., Yin, J., Duan, N., Brockschmidt, M., and Allamanis, M · 2021
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Efficient message passing for 0–1 ilps with binary decision diagrams
Lange, J.-H. and Swoboda, P · 2021
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Machine translation decoding beyond beam search
Leblond, R., Alayrac, J.-B., Sifre, L., Pislar, M., Lespiau, J.-B., Antonoglou, I., Simonyan, K., and Vinyals, O · 2021
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A token-level reference-free hallucination detection benchmark for free-form text generation
Liu, T., Zhang, Y., Brockett, C. J., Mao, Y., Sui, Z., Chen, W., and Dolan, W. B · 2021
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Understanding the properties of minimum Bayes risk decoding in neural machine translation
Müller, M. and Sennrich, R · 2021
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Firman, M., Campbell, N. D., Agapito, L., and Brostow, G. J · 2018
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Distilling knowledge for search-based structured prediction
Liu, Y., Che, W., Zhao, H., Qin, B., and Liu, T · 2018
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Analyzing uncertainty in neural machine translation
Ott, M., Auli, M., Grangier, D., and Ranzato, M · 2018
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Minimum word error rate training for attention-based sequence-to-sequence models
Prabhavalkar, R., Sainath, T. N., Wu, Y., Nguyen, P., Chen, Z., Chiu, C.-C., and Kannan, A · 2018
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The curious case of neural text degeneration
Holtzman, A., Buys, J., Forbes, M., and Choi, Y · 2019
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Learning and evaluating contextual embedding of source code
Kanade, A., Maniatis, P., Balakrishnan, G., and Shi, K · 2019
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Deep reinforcement learning for sequence-to-sequence models
Keneshloo, Y., Shi, T., Ramakrishnan, N., and Reddy, C. K · 2019
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Pearce, H. A., Ahmad, B., Tan, B., Dolan-Gavitt, B., and Karri, R · 2021
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Mining idioms in the wild
Sivaraman, A., Abreu, R., Scott, A. C., Akomolede, T., and Chandra, S · 2021
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LMdiff: A visual diff tool to compare language models
Strobelt, H., Hoover, B., Satyanarayan, A., and Gehrmann, S · 2021
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Perfection not required? human-AI partnerships in code translation
Weisz, J. D., Muller, M. J., Houde, S., Richards, J. T., Ross, S. I., Martinez, F., Agarwal, M., and Talamadupula, K · 2021
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Training a helpful and harmless assistant with reinforcement learning from human feedback
Bai, Y., Jones, A., Ndousse, K., Askell, A., Chen, A., DasSarma, N., Drain, D., Fort, S., Ganguli, D., Henighan, T., et al · 2022
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Grounded copilot: How programmers interact with code-generating models
Barke, S., James, M. B., and Polikarpova, N · 2022
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The calibration generalization gap
Carrell, A., Mallinar, N. R., Lucas, J., and Nakkiran, P · 2022
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Decision diagrams for discrete optimization: A survey of recent advances
Castro, M. P., Ciré, A. A., and Beck, J. C · 2022
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PaLM: Scaling language modeling with pathways
Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H. W., Sutton, C., Gehrmann, S., Schuh, P., Shi, K., Tsvyashchenko, S., Maynez, J., Rao, A., Barnes, P., Tay, Y., Shazeer, N. M., Prabhakaran, V., Reif, E., Du, N., Hutchinson, B. C., Pope, R., Bradbury, J., Austin, J., Isard, M., Gur-Ari, G., Yin, P., Duke, T., Levskaya, A., Ghemawat, S., Dev, S., Michalewski, H., García, X., Misra, V., Robinson, K., Fedus, L., Zhou, D., Ippolito, D., Luan, D., Lim, H., Zoph, B., Spiridonov, A., Sepassi, R., Dohan, D., Agrawal, S., Omernick, M., Dai, A. M., Pillai, T. S., Pellat, M., Lewkowycz, A., Moreira, E., Child, R., Polozov, O., Lee, K., Zhou, Z., Wang, X., Saeta, B., Díaz, M., Firat, O., Catasta, M., Wei, J., Meier-Hellstern, K. S., Eck, D., Dean, J., Petrov, S., and Fiedel, N · 2022
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High quality rather than high model probability: Minimum Bayes risk decoding with neural metrics
Freitag, M., Grangier, D., Tan, Q., and Liang, B · 2022
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Large language models can self-improve
Huang, J., Gu, S. S., Hou, L., Wu, Y., Wang, X., Yu, H., and Han, J · 2022
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Language models (mostly) know what they know
Kadavath, S., Conerly, T., Askell, A., Henighan, T. J., Drain, D., Perez, E., Schiefer, N., 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. B., Clark, J., Joseph, N., Mann, B., McCandlish, S., Olah, C., and Kaplan, J · 2022
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CodeRL: Mastering code generation through pretrained models and deep reinforcement learning
Le, H., Wang, Y., Gotmare, A. D., Savarese, S., and Hoi, S. C · 2022
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Competition-level code generation with alphacode
Li, Y., Choi, D., Chung, J., Kushman, N., Schrittwieser, J., Leblond, R., Eccles, T., Keeling, J., Gimeno, F., Dal Lago, A., et al · 2022
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Teaching models to express their uncertainty in words
Lin, S. C., Hilton, J., and Evans, O · 2022
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How readable is model-generated code? examining readability and visual inspection of github copilot
Madi, N. S. A · 2022
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Reading between the lines: Modeling user behavior and costs in ai-assisted programming
Mozannar, H., Bansal, G., Fourney, A., and Horvitz, E · 2022
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Codegen: An open large language model for code with multi-turn program synthesis
Nijkamp, E., Pang, B., Hayashi, H., Tu, L., Wang, H., Zhou, Y., Savarese, S., and Xiong, C · 2022
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Training language models to follow instructions with human feedback
Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C. L., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al · 2022
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Natural language to code translation with execution
Shi, F., Fried, D., Ghazvininejad, M., Zettlemoyer, L., and Wang, S. I · 2022
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Investigating explainability of generative ai for code through scenario-based design
Sun, J., Liao, Q. V., Muller, M. J., Agarwal, M., Houde, S., Talamadupula, K., and Weisz, J. D · 2022
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Plex: Towards reliability using pretrained large model extensions
Tran, D., Liu, J., Dusenberry, M. W., Phan, D., Collier, M., Ren, J. J., Han, K., Wang, Z., Mariet, Z. E., Hu, H., Band, N., Rudner, T. G. J., Singhal, K., Nado, Z., van Amersfoort, J. R., Kirsch, A., Jenatton, R., Thain, N., Yuan, H., Buchanan, E. K., Murphy, K., Sculley, D., Gal, Y., Ghahramani, Z., Snoek, J., and Lakshminarayanan, B · 2022
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Expectation vs. experience: Evaluating the usability of code generation tools powered by large language models
Upadhyaya, G., Reinhardt, A., Rajan, H., Kim, M., Glassman, E. L., Hartmann, B., and Pinedo, J · 2022
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Generation probabilities are not enough: Improving error highlighting for ai code suggestions
Vasconcelos, H., Bansal, G., Fourney, A., Liao, Q. V., and Wortman Vaughan, J · 2022
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Self-consistency improves chain of thought reasoning in language models
Wang, X., Wei, J., Schuurmans, D., Le, Q., hsin Chi, E. H., and Zhou, D · 2022
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Uncertainty quantification with pre-trained language models: A large-scale empirical analysis
Xiao, Y., Liang, P. P., Bhatt, U., Neiswanger, W., Salakhutdinov, R., and Morency, L.-P · 2022
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Coder reviewer reranking for code generation
Zhang, T., Yu, T., Hashimoto, T. B., Lewis, M., Yih, W.-t., Fried, D., and Wang, S. I · 2022
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