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TextGrad: Automatic "Differentiation" via Text

Paper · 2024 · Mert Yuksekgonul, Federico Bianchi, Joseph Boen et al.

Summary

Modern AI applications increasingly chain several LLMs with tools such as simulators, solvers, or search engines. These compound pipelines are usually tuned by hand, and their parts are not differentiable, so standard gradient training does not apply. The authors propose TextGrad, a framework that treats such a pipeline as a computation graph and borrows the idea of backpropagation. Instead of numerical gradients, an LLM writes natural-language critiques ("textual gradients") explaining how each variable should change to improve a downstream objective. These critiques are passed backward through arbitrary function calls, and an optimizer then rewrites the variables. The API deliberately mirrors PyTorch. Users only define the objective, with no task-specific prompt tuning. Unlike earlier prompt-focused systems such as DSPy and ProTeGi, TextGrad also optimizes individual solutions directly, including code, answers, molecules, and treatment plans. Experiments cover coding, scientific question answering, prompt optimization for reasoning, drug-like molecule design, and radiotherapy planning. The work matters because it offers one general, open-source way to automatically improve multi-component LLM systems.

Key points

  • On Google-Proof Question Answering (GPQA), refining answers at test time lifts GPT-4o zero-shot accuracy from 51% to 55%.
  • On LeetCode-Hard coding problems, TextGrad gives about a 20% relative gain over gpt-4o and the best prior method.
  • Prompt optimization brings GPT-3.5 close to GPT-4 on several reasoning tasks; one illustrated prompt rises from 77.8% to 91.9% accuracy.
  • PyTorch-style abstractions include tg.Variable, tg.BlackboxLLM, tg.TextLoss, and the tg.TGD optimizer, with loss.backward() and optimizer.step() calls.
  • The molecule and prostate-cancer radiotherapy results are proof-of-concept, validated only in silico (e.g., Vina scores). The code is released at github.com/zou-group/textgrad.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓Stanford UniversityStanford · United States310 works · 284,480 citations

📚 Shelves 4 Computer Science · 9,086 works Artificial Intelligence · 8,889 works Arts and Humanities · 9,296 works Language and Linguistics · 9,206 works

Cite

Cite as Mert Yuksekgonul, Federico Bianchi, Joseph Boen, Sheng Liu, Zhi Huang, Carlos Guestrin and James Zou. 2024. TextGrad: Automatic "Differentiation" via Text. arXiv:2406.07496.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.48550/arxiv.2406.07496 · arXiv 2406.07496

@misc{yuksekgonul2024textgrad,
  title = {{TextGrad: Automatic "Differentiation" via Text}},
  author = {Mert Yuksekgonul, Federico Bianchi, Joseph Boen, Sheng Liu, Zhi Huang, Carlos Guestrin and James Zou},
  year = {2024},
  doi = {10.48550/arxiv.2406.07496},
  eprint = {2406.07496},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2406.07496}
}

References

104 references, 33 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

  1. Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020). our card
  2. Achiam, J., Adler, S., Agarwal, S., Ahmad, L., Akkaya, I., Aleman, F. L., Almeida, D., Altenschmidt, J., Altman, S., Anadkat, S., et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023). our card
  3. Reid, M., Savinov, N., Teplyashin, D., Lepikhin, D., Lillicrap, T., Alayrac, J.-b., Soricut, R., Lazaridou, A., Firat, O., Schrittwieser, J., et al. Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context. arXiv preprint arXiv:2403.05530 (2024). our card
  4. AI@Meta. Llama 3 Model Card. https://github.com/meta-llama/llama3/blob/main/MODEL_ CARD.md (2024). ↗
  5. Anthropic, A. The Claude 3 Model Family: Opus, Sonnet, Haiku. Claude-3 Model Card (2024). find
  6. Bommasani, R., Hudson, D. A., Adeli, E., Altman, R., Arora, S., von Arx, S., Bernstein, M. S., Bohg, J., Bosselut, A., Brunskill, E., et al. On the opportunities and risks of foundation models. arXiv preprint arXiv:2108.07258 (2021). our card
  7. Trinh, T. H., Wu, Y., Le, Q. V., He, H. & Luong, T. Solving olympiad geometry without human demonstrations. Nature 625, 476–482 (2024). our card
  8. Li, Y., Choi, D., Chung, J., Kushman, N., Schrittwieser, J., Leblond, R., Eccles, T., Keeling, J., Gimeno, F., Dal Lago, A., et al. Competition-level code generation with alphacode. Science 378, 1092–1097 (2022). our card
  9. Yang, J., Jimenez, C. E., Wettig, A., Lieret, K., Yao, S., Narasimhan, K. & Press, O. SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering 2024. our card
  10. 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. & Potts, C. DSPy: Compiling Declarative Language Model Calls into State-of-the-Art Pipelines in The Twelfth International Conference on Learning Repres… find
  11. Zaharia, M., Khattab, O., Chen, L., Davis, J. Q., Miller, H., Potts, C., Zou, J., Carbin, M., Frankle, J., Rao, N. & Ghodsi, A. The Shift from Models to Compound AI Systems https://bair.berkeley.edu/ blog/2024/02/18/compound-ai-systems/. 2024. ↗
  12. Zhou, Y., Muresanu, A. I., Han, Z., Paster, K., Pitis, S., Chan, H. & Ba, J. Large Language Models are Human-Level Prompt Engineers in The Eleventh International Conference on Learning Representations (2023). https://openreview.net/forum?id=92gvk82DE-. ↗ our card
  13. Krizhevsky, A., Sutskever, I. & Hinton, G. E. Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012). find
  14. Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021). our card
  15. Fawzi, A., Balog, M., Huang, A., Hubert, T., Romera-Paredes, B., Barekatain, M., Novikov, A., R Ruiz, F. J., Schrittwieser, J., Swirszcz, G., et al. Discovering faster matrix multiplication algorithms with reinforcement learning. Nature 610, 47–53 (2022). find
  16. Mankowitz, D. J., Michi, A., Zhernov, A., Gelmi, M., Selvi, M., Paduraru, C., Leurent, E., Iqbal, S., Lespiau, J.-B., Ahern, A., et al. Faster sorting algorithms discovered using deep reinforcement learning. Nature 618, 257–263 (2023). find
  17. Merchant, A., Batzner, S., Schoenholz, S. S., Aykol, M., Cheon, G. & Cubuk, E. D. Scaling deep learning for materials discovery. Nature 624, 80–85 (2023). find
  18. Goodfellow, I., Bengio, Y. & Courville, A. Deep learning (MIT press, 2016). find
  19. Rumelhart, D. E., Hinton, G. E. & Williams, R. J. Learning representations by back-propagating errors. Nature 323, 533–536 (1986). Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S. & Darrell, T. Caffe: Convolutional Architecture for Fast Feature Embedding. arXi… our card
  20. Bergstra, J., Breuleux, O., Bastien, F., Lamblin, P., Pascanu, R., Desjardins, G., Turian, J., Warde-Farley, D. & Bengio, Y. Theano: A CPU and GPU Math Expression Compiler in Proceedings of the Python for Scientific Computing Conference (SciPy) (2010). find
  21. Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M., et al. TensorFlow: A System for Large-Scale Machine Learning in 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 16) (2016), 265–283. find
  22. Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al. Pytorch: An imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019). our card
  23. Collobert, R., Bengio, S. & Mariéthoz, J. Torch: a modular machine learning software library (2002). find
  24. Pryzant, R., Iter, D., Li, J., Lee, Y., Zhu, C. & Zeng, M. Automatic Prompt Optimization with “Gradient Descent” and Beam Search in Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing (eds Bouamor, H., Pino, J. & Bali, K.) (Association for Computational Linguistics… our card
  25. Shinn, N., Cassano, F., Gopinath, A., Narasimhan, K. & Yao, S. Reflexion: language agents with verbal reinforcement learning in Advances in Neural Information Processing Systems 36 (2023). https://proceedings.neurips.cc/paper_files/paper/2023/file/1b44b878bb782e6954cd888628510e90-Paper-Conferenc… ↗ our card
  26. Rein, D., Hou, B. L., Stickland, A. C., Petty, J., Pang, R. Y., Dirani, J., Michael, J. & Bowman, S. R. Gpqa: A graduate-level google-proof q&a benchmark. arXiv preprint arXiv:2311.12022 (2023). our card
  27. Li, X., Zhang, T., Dubois, Y., Taori, R., Gulrajani, I., Guestrin, C., Liang, P. & Hashimoto, T. B. Alpacaeval: An automatic evaluator of instruction-following models 2023. find
  28. Bai, Y., Jones, A., Ndousse, K., Askell, A., Chen, A., DasSarma, N., Drain, D., Fort, S., Ganguli, D., Henighan, T., et al. Training a helpful and harmless assistant with reinforcement learning from human feedback. arXiv preprint arXiv:2204.05862 (2022). find
  29. Madaan, A., Tandon, N., Gupta, P., Hallinan, S., Gao, L., Wiegreffe, S., Alon, U., Dziri, N., Prabhumoye, S., Yang, Y., et al. Self-refine: Iterative refinement with self-feedback. Advances in Neural Information Processing Systems 36 (2024). find
  30. Stiennon, N., Ouyang, L., Wu, J., Ziegler, D., Lowe, R., Voss, C., Radford, A., Amodei, D. & Christiano, P. F. Learning to summarize with human feedback. Advances in Neural Information Processing Systems 33, 3008–3021 (2020). find
  31. Yuan, W., Pang, R. Y., Cho, K., Sukhbaatar, S., Xu, J. & Weston, J. Self-rewarding language models. arXiv preprint arXiv:2401.10020 (2024). find
  32. Dubois, Y., Li, C. X., Taori, R., Zhang, T., Gulrajani, I., Ba, J., Guestrin, C., Liang, P. S. & Hashimoto, T. B. Alpacafarm: A simulation framework for methods that learn from human feedback. Advances in Neural Information Processing Systems 36 (2024). our card
  33. Bottou, L. Large-scale machine learning with stochastic gradient descent. Proceedings of COMPSTAT’2010, 177–186 (2010). find
  34. Boyd, S., Boyd, S. P. & Vandenberghe, L. Convex optimization (Cambridge university press, 2004). find
  35. Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al. Training language models to follow instructions with human feedback. Advances in neural information processing systems 35, 27730–27744 (2022). our card
  36. Wei, J., Bosma, M., Zhao, V., Guu, K., Yu, A. W., Lester, B., Du, N., Dai, A. M. & Le, Q. V. Finetuned Language Models are Zero-Shot Learners in International Conference on Learning Representations (2022). https://openreview.net/forum?id=gEZrGCozdqR. Yuksekgonul, M., Chandrasekaran, V., Jones, E., G… ↗
  37. Abdin, M. I., Gunasekar, S., Chandrasekaran, V., Li, J., Yuksekgonul, M., Peshawaria, R. G., Naik, R. & Nushi, B. KITAB: Evaluating LLMs on Constraint Satisfaction for Information Retrieval in The Twelfth International Conference on Learning Representations (2024). https://openreview.net / for… ↗
  38. Polyak, B. T. Some methods of speeding up the convergence of iteration methods. USSR Computational Mathematics and Mathematical Physics 4, 1–17 (1964). find
  39. Sutskever, I., Martens, J., Dahl, G. & Hinton, G. On the importance of initialization and momentum in deep learning in International conference on machine learning (2013), 1139–1147. find
  40. Sun, Y., Wang, X., Liu, Z., Miller, J., Efros, A. & Hardt, M. Test-Time Training with Self-Supervision for Generalization under Distribution Shifts in Proceedings of the 37th International Conference on Machine Learning (PMLR, 2020). https://proceedings.mlr.press/v119/sun20b.html. ↗
  41. Sun, Y., Li, X., Dalal, K., Hsu, C., Koyejo, S., Guestrin, C., Wang, X., Hashimoto, T. & Chen, X. Learning to (learn at test time). arXiv preprint arXiv:2310.13807 (2023). find
  42. Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K. R. & Cao, Y. ReAct: Synergizing Reasoning and Acting in Language Models in The Eleventh International Conference on Learning Representations (2023). https://openreview.net/forum?id=WE_vluYUL-X. ↗ our card
  43. Hendrycks, D., Burns, C., Basart, S., Zou, A., Mazeika, M., Song, D. & Steinhardt, J. Measuring Massive Multitask Language Understanding in International Conference on Learning Representations (2021). https://openreview.net/forum?id=d7KBjmI3GmQ. ↗ our card
  44. Kojima, T., Gu, S. S., Reid, M., Matsuo, Y. & Iwasawa, Y. Large language models are zero-shot reasoners. Advances in neural information processing systems 35, 22199–22213 (2022). our card
  45. Wei, J., Wang, X., Schuurmans, D., Bosma, M., Xia, F., Chi, E., Le, Q. V., Zhou, D., et al. Chain-of-thought prompting elicits reasoning in large language models. Advances in neural information processing systems 35, 24824–24837 (2022). our card
  46. OpenAI. Hello GPT-4o Accessed: 2024-05-18. 2024. https://openai.com/index/hello-gpt-4o/. ↗
  47. Liu, P., Yuan, W., Fu, J., Jiang, Z., Hayashi, H. & Neubig, G. Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing. ACM Computing Surveys 55, 1–35 (2023). our card
  48. Suzgun, M., Scales, N., Schärli, N., Gehrmann, S., Tay, Y., Chung, H. W., Chowdhery, A., Le, Q., Chi, E., Zhou, D. & Wei, J. Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them in Findings of the Association for Computational Linguistics: ACL 2023 (Association for Computational L… our card
  49. Srivastava, A. et al. Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models. Transactions on Machine Learning Research. ISSN : 2835-8856. https://openreview.net/forum?id=uyTL5Bvosj (2023). ↗ our card
  50. Cobbe, K., Kosaraju, V., Bavarian, M., Chen, M., Jun, H., Kaiser, L., Plappert, M., Tworek, J., Hilton, J., Nakano, R., Hesse, C. & Schulman, J. Training Verifiers to Solve Math Word Problems. arXiv preprint arXiv:2110.14168 (2021). our card
  51. Nicolaou, C. A. & Brown, N. Multi-objective optimization methods in drug design. Drug Discovery Today: Technologies 10, e427–e435 (2013). find
  52. Hoelder, S., Clarke, P. A. & Workman, P. Discovery of small molecule cancer drugs: successes, challenges and opportunities. Molecular oncology 6, 155–176 (2012). find
  53. Kontoyianni, M. Docking and virtual screening in drug discovery. Proteomics for drug discovery: Methods and protocols, 255–266 (2017). Agarwal, S. & Mehrotra, R. An overview of molecular docking. JSM chem 4, 1024–1028 (2016). find
  54. Trott, O. & Olson, A. J. AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. Journal of computational chemistry 31, 455– 461 (2010). find
  55. Ursu, O., Rayan, A., Goldblum, A. & Oprea, T. I. Understanding drug-likeness. Wiley Interdisciplinary Reviews: Computational Molecular Science 1, 760–781 (2011). find
  56. Bickerton, G. R., Paolini, G. V., Besnard, J., Muresan, S. & Hopkins, A. L. Quantifying the chemical beauty of drugs. Nature chemistry 4, 90–98 (2012). find
  57. Bender, B. J., Gahbauer, S., Luttens, A., Lyu, J., Webb, C. M., Stein, R. M., Fink, E. A., Balius, T. E., Carlsson, J., Irwin, J. J., et al. A practical guide to large-scale docking. Nature protocols 16, 4799–4832 (2021). find
  58. García-Ortegón, M., Simm, G. N., Tripp, A. J., Hernández-Lobato, J. M., Bender, A. & Bacallado, S. DOCKSTRING: easy molecular docking yields better benchmarks for ligand design. Journal of chemical information and modeling 62, 3486–3502 (2022). find
  59. Khan, F. M., Sperduto, P. W. & Gibbons, J. P. Khan’s Treatment Planning in Radiation Oncology:. (Lippincott Williams & Wilkins, 2021). find
  60. Webb, S. The physical basis of IMRT and inverse planning. The British journal of radiology 76, 678–689 (2003). find
  61. Hussein, M., Heijmen, B. J. M., Verellen, D. & Nisbet, A. Automation in Intensity Modulated Radiotherapy Treatment Planning—a Review of Recent Innovations. British Journal of Radiology 91, 20180270. ISSN : 0007-1285. (2024) (Dec. 2018). find
  62. Wieser, H.-P., Cisternas, E., Wahl, N., Ulrich, S., Stadler, A., Mescher, H., Müller, L.-R., Klinge, T., Gabrys, H., Burigo, L., et al. Development of the open-source dose calculation and optimization toolkit matRad. Medical Physics 44, 2556–2568 (2017). find
  63. Nori, H., Lee, Y. T., Zhang, S., Carignan, D., Edgar, R., Fusi, N., King, N., Larson, J., Li, Y., Liu, W., et al. Can generalist foundation models outcompete special-purpose tuning? case study in medicine. arXiv preprint arXiv:2311.16452 (2023). find
  64. Shin, T., Razeghi, Y., Logan IV, R. L., Wallace, E. & Singh, S. AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts in Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (Association for Computational Linguistics, Onli… our card
  65. Jia, M., Tang, L., Chen, B.-C., Cardie, C., Belongie, S., Hariharan, B. & Lim, S.-N. Visual prompt tuning in European Conference on Computer Vision (2022), 709–727. find
  66. Li, X. L. & Liang, P. Prefix-Tuning: Optimizing Continuous Prompts for Generation in Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) (Association for Computation… find
  67. Chen, X., Zhang, N., Xie, X., Deng, S., Yao, Y., Tan, C., Huang, F., Si, L. & Chen, H. Knowprompt: Knowledge-aware prompt-tuning with synergistic optimization for relation extraction in Proceedings of the ACM Web conference 2022 (2022), 2778–2788. find
  68. Ye, Q., Axmed, M., Pryzant, R. & Khani, F. Prompt engineering a prompt engineer. arXiv preprint arXiv:2311.05661 (2023). find
  69. Khattab, O., Santhanam, K., Li, X. L., Hall, D., Liang, P., Potts, C. & Zaharia, M. Demonstrate-Search-Predict: Composing Retrieval and Language Models for Knowledge-Intensive NLP. arXiv preprint arXiv:2212.14024 (2022). our card
  70. Singhvi, A., Shetty, M., Tan, S., Potts, C., Sen, K., Zaharia, M. & Khattab, O. DSPy Assertions: Computational Constraints for Self-Refining Language Model Pipelines. arXiv preprint arXiv:2312.13382 (2023). Yang, C., Wang, X., Lu, Y., Liu, H., Le, Q. V., Zhou, D. & Chen, X. Large Language Models as… our card
  71. Song, X., Tian, Y., Lange, R. T., Lee, C., Tang, Y. & Chen, Y. Position: Leverage Foundational Models for Black-Box Optimization 2024. arXiv: 2405.03547 [cs.LG]. — [arXiv:2405.03547](https://arxiv.org/abs/2405.03547) find
  72. Liu, T., Astorga, N., Seedat, N. & van der Schaar, M. Large Language Models to Enhance Bayesian Optimization in The Twelfth International Conference on Learning Representations (2024). https://openreview.net/forum?id=OOxotBmGol. ↗
  73. Wang, R., Zelikman, E., Poesia, G., Pu, Y., Haber, N. & Goodman, N. Hypothesis Search: Inductive Reasoning with Language Models in The Twelfth International Conference on Learning Representations (2024). https://openreview.net/forum?id=G7UtIGQmjm. ↗
  74. Gao, L., Dai, Z., Pasupat, P., Chen, A., Chaganty, A. T., Fan, Y., Zhao, V., Lao, N., Lee, H., Juan, D.-C. & Guu, K. RARR: Researching and Revising What Language Models Say, Using Language Models in Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Lo… our card
  75. Chen, X., Lin, M., Schärli, N. & Zhou, D. Teaching Large Language Models to Self-Debug in The Twelfth International Conference on Learning Representations (2024). https://openreview.net/forum ? id = KuPixIqPiq. ↗
  76. Shypula, A. G., Madaan, A., Zeng, Y., Alon, U., Gardner, J. R., Yang, Y., Hashemi, M., Neubig, G., Ranganathan, P., Bastani, O. & Yazdanbakhsh, A. Learning Performance-Improving Code Edits in The Twelfth International Conference on Learning Representations (2024). https://openreview.net/forum? id=ix… ↗
  77. Chase, H. LangChain Oct. 17, 2022. https://github.com/langchain-ai/langchain. ↗
  78. Liu, J. LlamaIndex https://github.com/jerryjliu/llamaindex. 2022. ↗
  79. Schick, T., Dwivedi-Yu, J., Dessì, R., Raileanu, R., Lomeli, M., Hambro, E., Zettlemoyer, L., Cancedda, N. & Scialom, T. Toolformer: Language models can teach themselves to use tools. Advances in Neural Information Processing Systems 36 (2024). our card
  80. Press, O., Zhang, M., Min, S., Schmidt, L., Smith, N. & Lewis, M. Measuring and Narrowing the Compositionality Gap in Language Models in Findings of the Association for Computational Linguistics: EMNLP 2023 (Association for Computational Linguistics, Singapore, Dec. 2023), 5687–5711. https://aclanth… find
  81. Zelikman, E., Wu, Y., Mu, J. & Goodman, N. STaR: Bootstrapping Reasoning With Reasoning in Advances in Neural Information Processing Systems (eds Koyejo, S., Mohamed, S., Agarwal, A., Belgrave, D., Cho, K. & Oh, A.) 35 (Curran Associates, Inc., 2022), 15476–15488. https://proceedings.neurips.cc/ pap… ↗ our card
  82. Huang, J., Gu, S., Hou, L., Wu, Y., Wang, X., Yu, H. & Han, J. Large Language Models Can Self-Improve in Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing (eds Bouamor, H., Pino, J. & Bali, K.) (Association for Computational Linguistics, Singapore, Dec. 2023). ht… our card
  83. Yang, K., Tian, Y., Peng, N. & Klein, D. Re3: Generating Longer Stories With Recursive Reprompting and Revision in Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (Association for Computational Linguistics, Dec. 2022). https://aclanthology.org/2022 . em… ↗
  84. Xie, Y., Kawaguchi, K., Zhao, Y., Zhao, X., Kan, M.-Y., He, J. & Xie, Q. Self-Evaluation Guided Beam Search for Reasoning in Thirty-seventh Conference on Neural Information Processing Systems (2023). https://openreview.net/forum?id=Bw82hwg5Q3. Paul, D., Ismayilzada, M., Peyrard, M., Borges, B., Bos… ↗ our card
  85. Zhao, A., Huang, D., Xu, Q., Lin, M., Liu, Y.-J. & Huang, G. Expel: Llm agents are experiential learners in Proceedings of the AAAI Conference on Artificial Intelligence 38 (2024), 19632–19642. find
  86. Le, H., Chen, H., Saha, A., Gokul, A., Sahoo, D. & Joty, S. CodeChain: Towards Modular Code Generation Through Chain of Self-revisions with Representative Sub-modules in The Twelfth International Conference on Learning Representations (2024). https://openreview.net/forum?id=vYhglxSj8j. ↗
  87. Robbins, H. & Monro, S. A stochastic approximation method. The Annals of Mathematical Statistics, 400–407 (1951). find
  88. Lewis, P., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., Goyal, N., Küttler, H., Lewis, M., Yih, W.-t., Rocktäschel, T., et al. Retrieval-augmented generation for knowledge-intensive nlp tasks. Advances in Neural Information Processing Systems 33, 9459–9474 (2020). find
  89. Robert, C. P. & Casella, G. Simulation and the Monte Carlo method. Springer Texts in Statistics, New York: Springer 2 (1999). find
  90. Kingma, D. & Ba, J. Adam: A Method for Stochastic Optimization in International Conference on Learning Representations (ICLR) (San Diega, CA, USA, 2015). find
  91. Lightman, H., Kosaraju, V., Burda, Y., Edwards, H., Baker, B., Lee, T., Leike, J., Schulman, J., Sutskever, I. & Cobbe, K. Let’s Verify Step by Step in The Twelfth International Conference on Learning Representations (2024). https://openreview.net/forum?id=v8L0pN6EOi. ↗ our card
  92. Schmidhuber, J. Evolutionary Principles in Self-Referential Learning PhD thesis (Technical University of Munich, 1987). find
  93. Thrun, S. & Pratt, L. in Learning to Learn 3–17 (Springer, 1998). find
  94. Finn, C., Abbeel, P. & Levine, S. Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks in Proceedings of the 34th International Conference on Machine Learning (2017). find
  95. RDKit: Open-source cheminformatics http://www.rdkit.org. ↗
  96. Hughes, J. P., Rees, S., Kalindjian, S. B. & Philpott, K. L. Principles of early drug discovery. British journal of pharmacology 162, 1239–1249 (2011). find
  97. Wenlock, M. C., Austin, R. P., Barton, P., Davis, A. M. & Leeson, P. D. A comparison of physiochemical property profiles of development and marketed oral drugs. Journal of medicinal chemistry 46, 1250–1256 (2003). find
  98. Knox, C., Wilson, M., Klinger, C. M., Franklin, M., Oler, E., Wilson, A., Pon, A., Cox, J., Chin, N. E., Strawbridge, S. A., et al. Drugbank 6.0: the drugbank knowledgebase for 2024. Nucleic Acids Research 52, D1265–D1275 (2024). find
  99. Berry, M., Fielding, B. & Gamieldien, J. Practical considerations in virtual screening and molecular docking. Emerging trends in computational biology, bioinformatics, and systems biology, 487 (2015). find
  100. Birhane, A., Kasirzadeh, A., Leslie, D. & Wachter, S. Science in the age of large language models. Nature Reviews Physics 5, 277–280 (2023). find
  101. Nikolova, N. & Jaworska, J. Approaches to measure chemical similarity–a review. QSAR & Combinatorial Science 22, 1006–1026 (2003). find
  102. Gaulton, A., Bellis, L. J., Bento, A. P., Chambers, J., Davies, M., Hersey, A., Light, Y., McGlinchey, S., Michalovich, D., Al-Lazikani, B., et al. ChEMBL: a large-scale bioactivity database for drug discovery. Nucleic acids research 40, D1100–D1107 (2012). find
  103. Swanson, K., Walther, P., Leitz, J., Mukherjee, S., Wu, J. C., Shivnaraine, R. V. & Zou, J. ADMET-AI: A machine learning ADMET platform for evaluation of large-scale chemical libraries. bioRxiv, 2023–12 (2023). find
  104. Xing, L., Li, J. G., Donaldson, S., Le, Q. T. & Boyer, A. L. Optimization of Importance Factors in Inverse Planning. Physics in Medicine & Biology 44, 2525. ISSN : 0031-9155. (2024) (Oct. 1999). find

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