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Large Language Models (LLMs) have demonstrated remarkable proficiency in understanding and generating natural language.
Interpretable numerical descriptors of amino acid space
Georgiev, A. G · 2009
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Quantifying the chemical beauty of drugs
Bickerton, G. R. J., Paolini, G. V., Besnard, J., Muresan, S., and Hopkins, A. L · 2012
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Predicting tumor cell line response to drug pairs with deep learning
Xia, F., Shukla, M., Brettin, T. S., Garcia-Cardona, C., Cohn, J. D., Allen, J. E., Maslov, S., Holbeck, S. L., Doroshow, J. H., Evrard, Y. A., Stahlberg, E. A., and Stevens, R. L · 2018
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Plug and play language models: A simple approach to controlled text generation
Dathathri, S., Madotto, A., Lan, J., Hung, J., Frank, E., Molino, P., Yosinski, J., and Liu, R · 2019
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Ctrl: A conditional transformer language model for controllable generation
Keskar, N. S., McCann, B., Varshney, L. R., Xiong, C., and Socher, R · 2019
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A transformer-based approach for source code summarization
Ahmad, W. U., Chakraborty, S., Ray, B., and Chang, K.-W · 2020
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Language models are few-shot learners
Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T. J., Child, R., Ramesh, A., Ziegler, D. M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., and Amodei, D · 2020
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Cocon: A self-supervised approach for controlled text generation
Chan, A., Ong, Y., Pung, B. T. W., Zhang, A., and Fu, J · 2020
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Beyond english-centric multilingual machine translation
Fan, A., Bhosale, S., Schwenk, H., Ma, Z., El-Kishky, A., Goyal, S., Baines, M., Çelebi, O., Wenzek, G., Chaudhary, V., Goyal, N., Birch, T., Liptchinsky, V., Edunov, S., Grave, E., Auli, M., and Joulin, A · 2020
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Improving massively multilingual neural machine translation and zero-shot translation
Zhang, B., Williams, P., Titov, I., and Sennrich, R · 2020
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Language models for the prediction of sars-cov-2 inhibitors
Blanchard, A. E., Gounley, J. P., Bhowmik, D., Shekar, M. C., Lyngaas, I., Gao, S., Yin, J., Tsaris, A., Wang, F., and Glaser, J · 2021
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The flores-101 evaluation benchmark for low-resource and multilingual machine translation
Goyal, N., Gao, C., Chaudhary, V., Chen, P.-J., Wenzek, G., Ju, D., Krishnan, S., Ranzato, M., Guzmán, F., and Fan, A · 2021
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Measuring mathematical problem solving with the math dataset
Hendrycks, D., Burns, C., Kadavath, S., Arora, A., Basart, S., Tang, E., Song, D., and Steinhardt, J · 2021
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Lora: Low-rank adaptation of large language models
Hu, J. E., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., and Chen, W · 2021
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Therapeutics data commons: Machine learning datasets and tasks for drug discovery and development
Huang, K., Fu, T., Gao, W., Zhao, Y., Roohani, Y., Leskovec, J., Coley, C. W., Xiao, C., Sun, J., and Zitnik, M · 2021
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Highly accurate protein structure prediction with alphafold
Jumper, J. M., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Zídek, A., Potapenko, A., Bridgland, A., Meyer, C., Kohl, S. A. A., Ballard, A., Cowie, A., Romera-Paredes, B., Nikolov, S., Jain, R., Adler, J., Back, T., Petersen, S., Reiman, D. A., Clancy, E., Zielinski, M., Steinegger, M., Pacholska, M., Berghammer, T., Bodenstein, S., Silver, D., Vinyals, O., Senior, A. W., Kavukcuoglu, K., Kohli, P., and Hassabis, D · 2021
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The power of scale for parameter-efficient prompt tuning
Lester, B., Al-Rfou, R., and Constant, N · 2021
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Prefix-tuning: Optimizing continuous prompts for generation
Li, X. L. and Liang, P · 2021
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Few-shot learning with multilingual generative language models
Lin, X. V., Mihaylov, T., Artetxe, M., Wang, T., Chen, S., Simig, D., Ott, M., Goyal, N., Bhosale, S., Du, J., Pasunuru, R., Shleifer, S., Koura, P. S., Chaudhary, V., O’Horo, B., Wang, J., Zettlemoyer, L., Kozareva, Z., Diab, M. T., Stoyanov, V., and Li, X · 2021
Cited alongside, same era.
Automl decathlon: Diverse tasks, modern methods, and efficiency at scale
Roberts, N., Guo, S., Xu, C., Talwalkar, A., Lander, D., Tao, L., Cai, L., Niu, S., Heng, J., Qin, H., Deng, M., Hog, J., Pfefferle, A., Shivakumar, S. A., Krishnakumar, A., Wang, Y., Sukthanker, R. S., Hutter, F., Hasanaj, E., Le, T.-D., Khodak, M., Nevmyvaka, Y., Rasul, K., Sala, F., Schneider, A., Shen, J., and Sparks, E. R · 2021
Cited alongside, same era.
Multitask prompted training enables zero-shot task generalization
Sanh, V., Webson, A., Raffel, C., Bach, S. H., Sutawika, L., Alyafeai, Z., Chaffin, A., Stiegler, A., Scao, T. L., Raja, A., Dey, M., Bari, M. S., Xu, C., Thakker, U., Sharma, S., Szczechla, E., Kim, T., Chhablani, G., Nayak, N. V., Datta, D., Chang, J. D., Jiang, M. T.-J., Wang, H., Manica, M., Shen, S., Yong, Z. X., Pandey, H., Bawden, R., Wang, T., Neeraj, T., Rozen, J., Sharma, A., Santilli, A., Févry, T., Fries, J. A., Teehan, R., Biderman, S. R., Gao, L., Bers, T., Wolf, T., and Rush, A. M · 2021
Cited alongside, same era.
Attribute alignment: Controlling text generation from pre-trained language models
A survey of controllable text generation using transformer-based pre-trained language models
Zhang, H., Song, H., Li, S., Zhou, M., and Song, D · 2022
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Unifying molecular and textual representations via multi-task language modelling
Christofidellis, D., Giannone, G., Born, J., Winther, O., Laino, T., and Manica, M · 2023
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What indeed can gpt models do in chemistry? a comprehensive benchmark on eight tasks
Guo, T., Guo, K., Liang, Z., Guo, Z., Chawla, N. V., Wiest, O., Zhang, X., et al · 2023
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Geneturing tests gpt models in genomics
Hou, W. and Ji, Z · 2023
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Mistral 7b, 2023
Jiang, A. Q., Sablayrolles, A., Mensch, A., Bamford, C., Chaplot, D. S., de las Casas, D., Bressand, F., Lengyel, G., Lample, G., Saulnier, L., Lavaud, L. R., Lachaux, M.-A., Stock, P., Scao, T. L., Lavril, T., Wang, T., Lacroix, T., and Sayed, W. E · 2023
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alphaXiv searches the wider corpus for related work and actual follow-ups.
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Yu, D., Sagae, K., and Yu, Z · 2021
Cited alongside, same era.
Bartsmiles: Generative masked language models for molecular representations
Chilingaryan, G., Tamoyan, H., Tevosyan, A., Babayan, N., Khondkaryan, L., Hambardzumyan, K., Navoyan, Z., Khachatrian, H., and Aghajanyan, A · 2022
Cited alongside, same era.
Lift: Language-interfaced fine-tuning for non-language machine learning tasks
Dinh, T., Zeng, Y., Zhang, R., Lin, Z., Rajput, S., Gira, M., yong Sohn, J., Papailiopoulos, D., and Lee, K · 2022
Cited alongside, same era.
Solving quantitative reasoning problems with language models
Lewkowycz, A., Andreassen, A., Dohan, D., Dyer, E., Michalewski, H., Ramasesh, V. V., Slone, A., Anil, C., Schlag, I., Gutman-Solo, T., Wu, Y., Neyshabur, B., Gur-Ari, G., and Misra, V · 2022
Cited alongside, same era.
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
Cited alongside, same era.
Controllable natural language generation with contrastive prefixes
Qian, J., Dong, L., Shen, Y., Wei, F., and Chen, W · 2022
Cited alongside, same era.
Bloom: A 176b-parameter open-access multilingual language model
Scao, T. L., Fan, A., Akiki, C., Pavlick, E.-J., Xie, . Z., Ye, Z., Bras, M., Belkada, Y., and Wolf, T · 2022
Cited alongside, same era.
Efficient architecture search for diverse tasks
Shen, J., Khodak, M., and Talwalkar, A · 2022
Cited alongside, same era.
Large language models encode clinical knowledge
Singhal, K., Azizi, S., Tu, T., Mahdavi, S., Wei, J., Chung, H. W., Scales, N., Tanwani, A. K., Cole-Lewis, H. J., Pfohl, S. J., Payne, P. A., Seneviratne, M. G., Gamble, P., Kelly, C., Scharli, N., Chowdhery, A., Mansfield, P. A., y Arcas, B. A., Webster, D. R., Corrado, G. S., Matias, Y., Chou, K. H.-L., Gottweis, J., Tomavsev, N., Liu, Y., Rajkomar, A., Barral, J. K., Semturs, C., Karthikesalingam, A., and Natarajan, V · 2022
Cited alongside, same era.
Lam, H. T., Sbodio, M. L., Galindo, M. M., Zayats, M., Fern’andez-D’iaz, R., Valls, V., Picco, G., Ramis, C. B., and L’opez, V · 2023
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Learning to compress prompts with gist tokens
Mu, J., Li, X. L., and Goodman, N. D · 2023
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Gpt-4 technical report
OpenAI · 2023
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Compositional task representations for large language models
Shao, N., Cai, Z., Xu, H., Liao, C., Zheng, Y., and Yang, Z · 2023
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Cross-modal fine-tuning: align then refine
Shen, J., Li, L., Dery, L. M., Staten, C., Khodak, M., Neubig, G., and Talwalkar, A · 2023
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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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Reprogramming pretrained language models for protein sequence representation learning
Vinod, R., Chen, P.-Y., and Das, P · 2023
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Reliability of medical information provided by chatgpt: Assessment against clinical guidelines and patient information quality instrument
Walker, H. L., Ghani, S., Kuemmerli, C., Nebiker, C. A., Müller, B. P., Raptis, D. A., and Staubli, S. M · 2023
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Chatbots in drug discovery: A case study on anti-cocaine addiction drug development with chatgpt
Wang, R., Feng, H., and Wei, G.-W · 2023
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Solving math word problems concerning systems of equations with gpt-3
Zong, M. L. and Krishnamachari, B · 2023
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Ups: Towards foundation models for pde solving via cross-modal adaptation, 2024
Shen, J., Marwah, T., and Talwalkar, A · 2024
Closest in time.
Yu, B., Baker, F. N., Chen, Z., Ning, X., and Sun, H · 2024
Closest in time.