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Large language models (LLMs) have displayed an impressive ability to harness natural language to perform complex tasks.
A deductive approach to program synthesis
Manna, Z. and Waldinger, R · 1980
Earlier work this paper cites.
Classification and Regression Trees
Breiman, L., Friedman, J. H., Olshen, R. A., and Stone, C. J · 1984
Earlier work this paper cites.
The swiss-prot protein sequence data bank
Bairoch, A. and Boeckmann, B · 1991
Earlier work this paper cites.
Symbolic regression via genetic programming
Augusto, D. A. and Barbosa, H. J · 2000
Earlier work this paper cites.
Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales
Pang, B. and Lee, L · 2005
Earlier work this paper cites.
Principles of data mining
Hand, D. J · 2007
Earlier work this paper cites.
Modeling annotators: A generative approach to learning from annotator rationales
Zaidan, O. and Eisner, J · 2008
Earlier work this paper cites.
Distilling free-form natural laws from experimental data
Schmidt, M. and Lipson, H · 2009
Earlier work this paper cites.
Recursive deep models for semantic compositionality over a sentiment treebank
Socher, R., Perelygin, A., Wu, J., Chuang, J., Manning, C. D., Ng, A., and Potts, C · 2013
Earlier work this paper cites.
Good debt or bad debt: Detecting semantic orientations in economic texts
Malo, P., Sinha, A., Korhonen, P., Wallenius, J., and Takala, P · 2014
Earlier work this paper cites.
Uniprot: a hub for protein information
Consortium, U · 2015
Earlier work this paper cites.
Pycortex: an interactive surface visualizer for fmri
Gao, J. S., Huth, A. G., Lescroart, M. D., and Gallant, J. L · 2015
Earlier work this paper cites.
Generating visual explanations
Hendricks, L. A., Akata, Z., Rohrbach, M., Donahue, J., Schiele, B., and Darrell, T · 2016
Earlier work this paper cites.
Natural speech reveals the semantic maps that tile human cerebral cortex
Huth, A. G., De Heer, W. A., Griffiths, T. L., Theunissen, F. E., and Gallant, J. L · 2016
Earlier work this paper cites.
Why should i trust you?: Explaining the predictions of any classifier
Ribeiro, M. T., Singh, S., and Guestrin, C · 2016
Earlier work this paper cites.
Program synthesis
Gulwani, S., Polozov, O., Singh, R., et al · 2017
Earlier work this paper cites.
Detecting statistical interactions from neural network weights
Tsang, M., Cheng, D., and Liu, Y · 2017
Earlier work this paper cites.
e-snli: Natural language inference with natural language explanations
Camburu, O.-M., Rocktäschel, T., Lukasiewicz, T., and Blunsom, P · 2018
Earlier work this paper cites.
What you can cram into a single vector: Probing sentence embeddings for linguistic properties
Conneau, A., Kruszewski, G., Lample, G., Barrault, L., and Baroni, M · 2018
Earlier work this paper cites.
Bert: Pre-training of deep bidirectional transformers for language understanding
Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K · 2018
Earlier work this paper cites.
Table-to-text generation by structure-aware seq2seq learning
Liu, T., Wang, K., Sha, L., Chang, B., and Sui, Z · 2018
Earlier work this paper cites.
The building blocks of interpretability
Olah, C., Satyanarayan, A., Johnson, I., Carter, S., Schubert, L., Ye, K., and Mordvintsev, A · 2018
Earlier work this paper cites.
Distill-and-compare: Auditing black-box models using transparent model distillation
Tan, S., Caruana, R., Hooker, G., and Lou, Y · 2018
Earlier work this paper cites.
Scibert: A pretrained language model for scientific text
Beltagy, I., Lo, K., and Cohan, A · 2019
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The curious case of neural text degeneration
Holtzman, A., Buys, J., Du, L., Forbes, M., and Choi, Y · 2019
Cited alongside, same era.
Neural text summarization: A critical evaluation
Kryściński, W., Keskar, N. S., McCann, B., Xiong, C., and Socher, R · 2019
Cited alongside, same era.
Incorporating priors with feature attribution on text classification
Liu, F. and Avci, B · 2019
Cited alongside, same era.
Explainable ai for trees: From local explanations to global understanding
GPT-J-6B: A 6 Billion Parameter Autoregressive Language Model
Wang, B. and Komatsuzaki, A · 2021
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Inferbert: a transformer-based causal inference framework for enhancing pharmacovigilance
Wang, X., Xu, X., Tong, W., Roberts, R., and Liu, Z · 2021
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Do prompt-based models really understand the meaning of their prompts?
Webson, A. and Pavlick, E · 2021
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Adapting language models for zero-shot learning by meta-tuning on dataset and prompt collections
Zhong, R., Lee, K., Zhang, Z., and Klein, D · 2021
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Hierarchical shrinkage: improving the accuracy and interpretability of tree-based methods
Agarwal, A., Tan, Y. S., Ronen, O., Singh, C., and Yu, B · 2022
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Lundberg, S. M., Erion, G., Chen, H., DeGrave, A., Prutkin, J. M., Nair, B., Katz, R., Himmelfarb, J., Bansal, N., and Lee, S.-I · 2019
Cited alongside, same era.
Language models as knowledge bases?
Petroni, F., Rocktäschel, T., Lewis, P., Bakhtin, A., Wu, Y., Miller, A. H., and Riedel, S · 2019
Cited alongside, same era.
Language models are unsupervised multitask learners
Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al · 2019
Cited alongside, same era.
Hierarchical interpretations for neural network predictions
Singh, C., Murdoch, W. J., and Yu, B · 2019
Cited alongside, same era.
Universal adversarial triggers for attacking and analyzing nlp
Wallace, E., Feng, S., Kandpal, N., Gardner, M., and Singh, S · 2019
Cited alongside, same era.
Language models are few-shot learners
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al · 2020
Cited alongside, same era.
Exploring the limits of transfer learning with a unified text-to-text transformer
Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., Liu, P. J., et al · 2020
Cited alongside, same era.
The tox21 10k compound library: collaborative chemistry advancing toxicology
Richard, A. M., Huang, R., Waidyanatha, S., Shinn, P., Collins, B. J., Thillainadarajah, I., Grulke, C. M., Williams, A. J., Lougee, R. R., Judson, R. S., et al · 2020
Cited alongside, same era.
Closest in time.
Promptsource: An integrated development environment and repository for natural language prompts
Bach, S. H., Sanh, V., Yong, Z.-X., Webson, A., Raffel, C., Nayak, N. V., Sharma, A., Kim, T., Bari, M. S., Fevry, T., et al · 2022
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Gpt-neox-20b: An open-source autoregressive language model
Black, S., Biderman, S., Hallahan, E., Anthony, Q., Gao, L., Golding, L., He, H., Leahy, C., McDonell, K., Phang, J., et al · 2022
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Scaling instruction-finetuned language models, 2022
Chung, H. W., Hou, L., Longpre, S., Zoph, B., Tay, Y., Fedus, W., Li, E., Wang, X., Dehghani, M., Brahma, S., Webson, A., Gu, S. S., Dai, Z., Suzgun, M., Chen, X., Chowdhery, A., Narang, S., Mishra, G., Yu, A., Zhao, V., Huang, Y., Dai, A., Yu, H., Petrov, S., Chi, E. H., Dean, J., Devlin, J., Roberts, A., Zhou, D., Le, Q. V., and Wei, J · 2022
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Rlprompt: Optimizing discrete text prompts with reinforcement learning
Deng, M., Wang, J., Hsieh, C.-P., Wang, Y., Guo, H., Shu, T., Song, M., Xing, E. P., and Hu, Z · 2022
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Instruction induction: From few examples to natural language task descriptions
Honovich, O., Shaham, U., Bowman, S. R., and Levy, O · 2022
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Solving quantitative reasoning problems with language models
Lewkowycz, A., Andreassen, A., Dohan, D., Dyer, E., Michalewski, H., Ramasesh, V., Slone, A., Anil, C., Schlag, I., Gutman-Solo, T., et al · 2022
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Cutting down on prompts and parameters: Simple few-shot learning with language models
Logan IV, R., Balazevic, I., Wallace, E., Petroni, F., Singh, S., and Riedel, S · 2022
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Fantastically ordered prompts and where to find them: Overcoming few-shot prompt order sensitivity
Lu, Y., Bartolo, M., Moore, A., Riedel, S., and Stenetorp, P · 2022
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Locating and editing factual knowledge in gpt
Meng, K., Bau, D., Andonian, A., and Belinkov, Y · 2022
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Scinli: A corpus for natural language inference on scientific text
Sadat, M. and Caragea, C · 2022
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Emb-gam: an interpretable and efficient predictor using pre-trained language models
Singh, C. and Gao, J · 2022
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Interactive and visual prompt engineering for ad-hoc task adaptation with large language models
Strobelt, H., Webson, A., Sanh, V., Hoover, B., Beyer, J., Pfister, H., and Rush, A. M · 2022
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Fast interpretable greedy-tree sums (figs)
Tan, Y. S., Singh, C., Nasseri, K., Agarwal, A., and Yu, B · 2022
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Galactica: A large language model for science
Taylor, R., Kardas, M., Cucurull, G., Scialom, T., Hartshorn, A., Saravia, E., Poulton, A., Kerkez, V., and Stojnic, R · 2022
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Benchmarking generalization via in-context instructions on 1,600+ language tasks
Wang, Y., Mishra, S., Alipoormolabashi, P., Kordi, Y., et al · 2022
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Opt: Open pre-trained transformer language models
Zhang, S., Roller, S., Goyal, N., Artetxe, M., Chen, M., Chen, S., Dewan, C., Diab, M., Li, X., Lin, X. V., et al · 2022
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Describing differences between text distributions with natural language
Zhong, R., Snell, C., Klein, D., and Steinhardt, J · 2022
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Large language models are human-level prompt engineers
Zhou, Y., Muresanu, A. I., Han, Z., Paster, K., Pitis, S., Chan, H., and Ba, J · 2022
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