Fetching the paper…
Reading the bibliography…
Although large language models (LLMs) have shown promise in biomolecule optimization problems, they incur heavy computational costs and struggle to satisfy precise constraints.
Tener: Adapting transformer encoder for named entity recognition, 2019
Yan, H., Deng, B., Li, X., and Qiu, X · 1911
Earlier work this paper cites.
Adaptation in tunably rugged fitness landscapes: The rough mount fuji model
Neidhart, J., Szendro, I. G., and Krug, J · 1943
Earlier work this paper cites.
Rational choice and the structure of the environment
Simon, H. A · 1956
Earlier work this paper cites.
Information theory and statistical mechanics
Jaynes, E. T · 1957
Earlier work this paper cites.
Individual choice behavior , volume 4
Luce, R. D. et al · 1959
Earlier work this paper cites.
On bayesian methods for seeking the extremum
Močkus, J · 1975
Earlier work this paper cites.
Simple statistical gradient-following algorithms for connectionist reinforcement learning
Williams, R. J · 1992
Earlier work this paper cites.
Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
Back, T · 1996
Earlier work this paper cites.
Design by directed evolution
Arnold, F. H · 1998
Earlier work this paper cites.
Efficient global optimization of expensive black-box functions
Jones, D. R., Schonlau, M., and Welch, W. J · 1998
Earlier work this paper cites.
Population genetics: a concise guide
Gillespie, J. H · 2004
Earlier work this paper cites.
Test functions for optimization needs
Molga, M. and Smutnicki, C · 2005
Earlier work this paper cites.
The foldx web server: an online force field
Schymkowitz, J., Borg, J., Stricher, F., Nys, R., Rousseau, F., and Serrano, L · 2005
Earlier work this paper cites.
Pyrosetta: a script-based interface for implementing molecular modeling algorithms using rosetta
Chaudhury, S., Lyskov, S., and Gray, J. J · 2010
Earlier work this paper cites.
Role of conformational sampling in computing mutation-induced changes in protein structure and stability
Kellogg, E. H., Leaver-Fay, A., and Baker, D · 2011
Earlier work this paper cites.
Thermodynamics as a theory of decision-making with information-processing costs
Ortega, P. A. and Braun, D. A · 2013
Earlier work this paper cites.
Monte Carlo theory, methods and examples
Owen, A. B · 2013
Earlier work this paper cites.
Local fitness landscape of the green fluorescent protein
Sarkisyan, K. S., Bolotin, D. A., Meer, M. V., Usmanova, D. R., Mishin, A. S., Sharonov, G. V., Ivankov, D. N., Bozhanova, N. G., Baranov, M. S., Soylemez, O., et al · 2016
Earlier work this paper cites.
Adaptation in protein fitness landscapes is facilitated by indirect paths
Wu, N. C., Dai, L., Olson, C. A., Lloyd-Smith, J. O., and Sun, R · 2016
Earlier work this paper cites.
Proximal policy optimization algorithms, 2017
Schulman, J., Wolski, F., Dhariwal, P., Radford, A., and Klimov, O · 2017
Earlier work this paper cites.
Flex ddg: Rosetta ensemble-based estimation of changes in protein–protein binding affinity upon mutation
Barlow, K. A., O Conchuir, S., Thompson, S., Suresh, P., Lucas, J. E., Heinonen, M., and Kortemme, T · 2018
Earlier work this paper cites.
Semantic parsing for task oriented dialog using hierarchical representations
Gupta, S., Shah, R., Mohit, M., Kumar, A., and Lewis, M · 2018
Earlier work this paper cites.
Tuning multilingual transformers for language-specific named entity recognition
Arkhipov, M., Trofimova, M., Kuratov, Y., and Sorokin, A · 2019
Earlier work this paper cites.
Guacamol: benchmarking models for de novo molecular design
Brown, N., Fiscato, M., Segler, M. H., and Vaucher, A. C · 2019
Earlier work this paper cites.
CTRL - A Conditional Transformer Language Model for Controllable Generation
Keskar, N. S., McCann, B., Varshney, L., Xiong, C., and Socher, R · 2019
Earlier work this paper cites.
Generalized variational inference: Three arguments for deriving new posteriors
Knoblauch, J., Jewson, J., and Damoulas, T · 2019
Earlier work this paper cites.
Comprehensive aav capsid fitness landscape reveals a viral gene and enables machine-guided design
Ogden, P. J., Kelsic, E. D., Sinai, S., and Church, G. M · 2019
Earlier work this paper cites.
Pytorch: An imperative style, high-performance deep learning library
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al · 2019
Earlier work this paper cites.
Language models are unsupervised multitask learners
Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., and Sutskever, I · 2019
Earlier work this paper cites.
Evaluating protein transfer learning with tape
Rao, R., Bhattacharya, N., Thomas, N., Duan, Y., Chen, X., Canny, J., Abbeel, P., and Song, Y. S · 2019
Earlier work this paper cites.
Generating logical forms from graph representations of text and entities
Shaw, P., Massey, P., Chen, A., Piccinno, F., and Altun, Y · 2019
Earlier work this paper cites.
Fine-tuning language models from human preferences
Ziegler, D. M., Stiennon, N., Wu, J., Brown, T. B., Radford, A., Amodei, D., Christiano, P., and Irving, G · 2019
Earlier work this paper cites.
Population-based black-box optimization for biological sequence design
Angermueller, C., Belanger, D., Gane, A., Mariet, Z., Dohan, D., Murphy, K., Colwell, L., and Sculley, D · 2020
Earlier work this paper cites.
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., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D., 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
Earlier work this paper cites.
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 · 2020
Earlier work this paper cites.
The Pile: An 800gb dataset of diverse text for language modeling
Gao, L., Biderman, S., Black, S., Golding, L., Hoppe, T., Foster, C., Phang, J., He, H., Thite, A., Nabeshima, N., Presser, S., and Leahy, C · 2020
Earlier work this paper cites.
Reward-rational (implicit) choice: A unifying formalism for reward learning
Jeon, H. J., Milli, S., and Dragan, A · 2020
Earlier work this paper cites.
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., and Liu, P. J · 2020
Earlier work this paper cites.
Don’t parse, generate! a sequence to sequence architecture for task-oriented semantic parsing
Rongali, S., Soldaini, L., Monti, E., and Hamza, W · 2020
Earlier work this paper cites.
Graph-based transformer with cross-candidate verification for semantic parsing
Shao, B., Gong, Y., Qi, W., Cao, G., Ji, J., and Lin, X · 2020
Earlier work this paper cites.
Adalead: A simple and robust adaptive greedy search algorithm for sequence design
Sinai, S., Wang, R., Whatley, A., Slocum, S., Locane, E., and Kelsic, E. D · 2020
Earlier work this paper cites.
Learning to summarize with human feedback
Stiennon, N., Ouyang, L., Wu, J., Ziegler, D., Lowe, R., Voss, C., Radford, A., Amodei, D., and Christiano, P. F · 2020
Earlier work this paper cites.
Consistency of a recurrent language model with respect to incomplete decoding
Welleck, S., Kulikov, I., Kim, J., Pang, R. Y., and Cho, K · 2020
Earlier work this paper cites.
T2NER: Transformers based transfer learning framework for named entity recognition
Amin, S. and Neumann, G · 2021
Earlier work this paper cites.
On the opportunities and risks of foundation models
Bommasani, R., Hudson, D. A., Adeli, E., Altman, R., Arora, S., von Arx, S., Bernstein, M. S., Bohg, J., Bosselut, A., Brunskill, E., Brynjolfsson, E., Buch, S., Card, D., Castellon, R., Chatterji, N. S., Chen, A. S., Creel, K. A., Davis, J., Demszky, D., Donahue, C., Doumbouya, M., Durmus, E., Ermon, S., Etchemendy, J., Ethayarajh, K., Fei-Fei, L., Finn, C., Gale, T., Gillespie, L. E., Goel, K., Goodman, N. D., Grossman, S., Guha, N., Hashimoto, T., Henderson, P., Hewitt, J., Ho, D. E., Hong, J., Hsu, K., Huang, J., Icard, T. F., Jain, S., Jurafsky, D., Kalluri, P., Karamcheti, S., Keeling, G., Khani, F., Khattab, O., Koh, P. W., Krass, M. S., Krishna, R., Kuditipudi, R., Kumar, A., Ladhak, F., Lee, M., Lee, T., Leskovec, J., Levent, I., Li, X. L., Li, X., Ma, T., Malik, A., Manning, C. D., Mirchandani, S. P., Mitchell, E., Munyikwa, Z., Nair, S., Narayan, A., Narayanan, D., Newman, B., Nie, A., Niebles, J. C., Nilforoshan, H., Nyarko, J. F., Ogut, G., Orr, L., Papadimitriou, I., Park, J. S., Piech, C., Portelance, E., Potts, C., Raghunathan, A., Reich, R., Ren, H., Rong, F., Roohani, Y. H., Ruiz, C., Ryan, J., R’e, C., Sadigh, D., Sagawa, S., Santhanam, K., Shih, A., Srinivasan, K. P., Tamkin, A., Taori, R., Thomas, A. W., Tramèr, F., Wang, R. E., Wang, W., Wu, B., Wu, J., Wu, Y., Xie, S. M., Yasunaga, M., You, J., Zaharia, M. A., Zhang, M., Zhang, T., Zhang, X., Zhang, Y., Zheng, L., Zhou, K., and Liang, P · 2021
Earlier work this paper cites.
Deep extrapolation for attribute-enhanced generation
Chan, A., Madani, A., Krause, B., and Naik, N · 2021
Cited alongside, same era.
Evaluating large language models trained on code
Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H. P. D. O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al · 2021
Cited alongside, same era.
Function-guided protein design by deep manifold sampling
Gligorijević, V., Berenberg, D., Ra, S., Watkins, A., Kelow, S., Cho, K., and Bonneau, R · 2021
Cited alongside, same era.
Accelerating high-throughput virtual screening through molecular pool-based active learning
Graff, D. E., Shakhnovich, E. I., and Coley, C. W · 2021
Cited alongside, same era.
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
Direct preference optimization: Your language model is secretly a reward model
Rafailov, R., Sharma, A., Mitchell, E., Manning, C. D., Ermon, S., and Finn, C · 2023
Later among the works it cites.
Mathematical discoveries from program search with large language models
Romera-Paredes, B., Barekatain, M., Novikov, A., Balog, M., Kumar, M. P., Dupont, E., Ruiz, F. J. R., Ellenberg, J. S., Wang, P., Fawzi, O., Kohli, P., and Fawzi, A · 2023
Later among the works it cites.
Evaluating large language models on controlled generation tasks
Sun, J., Tian, Y., Zhou, W., Xu, N., Hu, Q., Gupta, R., Wieting, J., Peng, N., and Ma, X · 2023
Later among the works it cites.
The behavior and convergence of local bayesian optimization
Wu, K., Kim, K., Garnett, R., and Gardner, J · 2023
Later among the works it cites.
Using large language models for hyperparameter optimization
Zhang, M., Desai, N., Bae, J., Lorraine, J., and Ba, J · 2023
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
DExperts: Decoding-time controlled text generation with experts and anti-experts
Liu, A., Sap, M., Lu, X., Swayamdipta, S., Bhagavatula, C., Smith, N. A., and Choi, Y · 2021
Cited alongside, same era.
Optimization of therapeutic antibodies by predicting antigen specificity from antibody sequence via deep learning
Mason, D. M., Friedensohn, S., Weber, C. R., Jordi, C., Wagner, B., Meng, S. M., Ehling, R. A., Bonati, L., Dahinden, J., Gainza, P., et al · 2021
Cited alongside, same era.
A plug-and-play method for controlled text generation
Pascual, D., Egressy, B., Meister, C., Cotterell, R., and Wattenhofer, R · 2021
Cited alongside, same era.
Learning contextual representations for semantic parsing with generation-augmented pre-training
Shi, P., Ng, P., Wang, Z., Zhu, H., Li, A. H., Wang, J., dos Santos, C. N., and Xiang, B · 2021
Cited alongside, same era.
Joint universal syntactic and semantic parsing
Stengel-Eskin, E., Murray, K., Zhang, S., White, A. S., and Van Durme, B · 2021
Cited alongside, same era.
A fresh look at de novo molecular design benchmarks
Tripp, A., Simm, G. N. C., and Hernández-Lobato, J. M · 2021
Cited alongside, same era.
T-NER: An all-round python library for transformer-based named entity recognition
Ushio, A. and Camacho-Collados, J · 2021
Cited alongside, same era.
Zheng, X., Lin, H., Han, X., and Sun, L · 2023
Later among the works it cites.
Controlled text generation with natural language instructions
Zhou, W., Jiang, Y. E., Wilcox, E., Cotterell, R., and Sachan, M · 2023
Later among the works it cites.
Back to basics: Revisiting REINFORCE-style optimization for learning from human feedback in LLMs
Ahmadian, A., Cremer, C., Gallé, M., Fadaee, M., Kreutzer, J., Pietquin, O., Üstün, A., and Hooker, S · 2024
Closest in time.
Optimus: Scalable optimization modeling with (mi) lp solvers and large language models
AhmadiTeshnizi, A., Gao, W., and Udell, M · 2024
Closest in time.
Ahmed, T. and Choudhury, S · 2024
Closest in time.
Baselining the buzz. trastuzumab-her2 affinity, and beyond!
Chinery, L., Hummer, A. M., Mehta, B. B., Akbar, R., Rawat, P., Slabodkin, A., Le Quy, K., Lund-Johansen, F., Greiff, V., Jeliazkov, J. R., et al · 2024
Closest in time.
Controlled text generation via language model arithmetic
Dekoninck, J., Fischer, M., Beurer-Kellner, L., and Vechev, M · 2024
Closest in time.
Towards analyzing and understanding the limitations of dpo: A theoretical perspective, 2024
Feng, D., Qin, B., Huang, C., Zhang, Z., and Lei, W · 2024
Closest in time.
Protein design with guided discrete diffusion
Gruver, N., Stanton, S., Frey, N., Rudner, T. G., Hotzel, I., Lafrance-Vanasse, J., Rajpal, A., Cho, K., and Wilson, A. G · 2024
Closest in time.
Efficient evolution of human antibodies from general protein language models
Hie, B. L., Shanker, V. R., Xu, D., Bruun, T. U., Weidenbacher, P. A., Tang, S., Wu, W., Pak, J. E., and Kim, P. S · 2024
Closest in time.
New desiderata for direct preference optimization
Hu, X., He, T., and Wipf, D · 2024
Closest in time.
Concept bottleneck language models for protein design
Ismail, A. A., Oikarinen, T., Wang, A., Adebayo, J., Stanton, S., Joren, T., Kleinhenz, J., Goodman, A., Bravo, H. C., Cho, K., et al · 2024
Closest in time.
A combinatorially complete epistatic fitness landscape in an enzyme active site
Johnston, K. E., Almhjell, P. J., Watkins-Dulaney, E. J., Liu, G., Porter, N. J., Yang, J., and Arnold, F. H · 2024
Closest in time.
DSPy: Compiling declarative language model calls into state-of-the-art pipelines
Khattab, O., Singhvi, A., Maheshwari, P., Zhang, Z., Santhanam, K., A, S. V., Haq, S., Sharma, A., Joshi, T. T., Moazam, H., Miller, H., Zaharia, M., and Potts, C · 2024
Closest in time.
Understanding the effects of RLHF on LLM generalisation and diversity
Kirk, R., Mediratta, I., Nalmpantis, C., Luketina, J., Hambro, E., Grefenstette, E., and Raileanu, R · 2024
Closest in time.
Large language models as evolution strategies
Lange, R., Tian, Y., and Tang, Y · 2024
Closest in time.
Exploring mathematical extrapolation of large language models with synthetic data
Li, H., Ma, Y., Zhang, Y., Ye, C., and Chen, J · 2024
Closest in time.
Large language models as evolutionary optimizers
Liu, S., Chen, C., Qu, X., Tang, K., and Ong, Y.-S · 2024
Closest in time.
LLM and simulation as bilevel optimizers: A new paradigm to advance physical scientific discovery
Ma, P., Wang, T.-H., Guo, M., Sun, Z., Tenenbaum, J. B., Rus, D., Gan, C., and Matusik, W · 2024
Closest in time.
The hidden infinity in preference learning, July 2024
Malladi, S · 2024
Closest in time.
SimPO: Simple preference optimization with a reference-free reward
Meng, Y., Xia, M., and Chen, D · 2024
Closest in time.
Language model crossover: Variation through few-shot prompting
Meyerson, E., Nelson, M. J., Bradley, H., Gaier, A., Moradi, A., Hoover, A. K., and Lehman, J · 2024
Closest in time.
Puzzlebench: Can llms solve challenging first-order combinatorial reasoning problems?, 2024
Mittal, C., Kartik, K., Mausam, and Singla, P · 2024
Closest in time.
Llmatic: neural architecture search via large language models and quality diversity optimization
Nasir, M. U., Earle, S., Togelius, J., James, S., and Cleghorn, C · 2024
Closest in time.
Does writing with language models reduce content diversity?, 2024
Padmakumar, V. and He, H · 2024
Closest in time.
Smaug: Fixing failure modes of preference optimisation with dpo-positive, 2024
Pal, A., Karkhanis, D., Dooley, S., Roberts, M., Naidu, S., and White, C · 2024
Closest in time.
Iterative reasoning preference optimization, 2024
Pang, R. Y., Yuan, W., Cho, K., He, H., Sukhbaatar, S., and Weston, J · 2024
Closest in time.
A long way to go: Investigating length correlations in RLHF, 2024
Singhal, P., Goyal, T., Xu, J., and Durrett, G · 2024
Closest in time.
Position: Leverage foundational models for black-box optimization
Song, X., Tian, Y., Lange, R. T., Lee, C., Tang, Y., and Chen, Y · 2024
Closest in time.
Variational search distributions
Steinberg, D. M., Oliveira, R., Ong, C. S., and Bonilla, E. V · 2024
Closest in time.
Implicitly guided design with propen: Match your data to follow the gradient, 2024
Tagasovska, N., Gligorijević, V., Cho, K., and Loukas, A · 2024
Closest in time.
Understanding the performance gap between online and offline alignment algorithms, 2024
Tang, Y., Guo, D. Z., Zheng, Z., Calandriello, D., Cao, Y., Tarassov, E., Munos, R., Ávila Pires, B., Valko, M., Cheng, Y., and Dabney, W · 2024
Closest in time.
Secrets of rlhf in large language models part ii: Reward modeling, 2024
Wang, B., Zheng, R., Chen, L., Liu, Y., Dou, S., Huang, C., Shen, W., Jin, S., Zhou, E., Shi, C., Gao, S., Xu, N., Zhou, Y., Fan, X., Xi, Z., Zhao, J., Wang, X., Ji, T., Yan, H., Shen, L., Chen, Z., Gui, T., Zhang, Q., Qiu, X., Huang, X., Wu, Z., and Jiang, Y.-G · 2024
Closest in time.
Stopping bayesian optimization with probabilistic regret bounds
Wilson, J · 2024
Closest in time.
Large language models as optimizers
Yang, C., Wang, X., Lu, Y., Liu, H., Le, Q. V., Zhou, D., and Chen, X · 2024
Closest in time.
Following length constraints in instructions, 2024
Yuan, W., Kulikov, I., Yu, P., Cho, K., Sukhbaatar, S., Weston, J., and Xu, J · 2024
Closest in time.
Sequence to sequence reward modeling: Improving rlhf by language feedback, 2024
Zhou, J., Ji, J., Dai, J., and Yang, Y · 2024
Closest in time.
Distilling mathematical reasoning capabilities into small language models
Zhu, X., Li, J., Liu, Y., Ma, C., and Wang, W · 2024
Closest in time.
Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning
Guo, D., Yang, D., Zhang, H., Song, J., Zhang, R., Xu, R., Zhu, Q., Ma, S., Wang, P., Bi, X., et al · 2025
Closest in time.