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The ability to generate diverse solutions to a given problem is a hallmark of human creativity.
Divergent thinking
Runco, M. A · 1991
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A tutorial on energy-based learning
LeCun, Y., Chopra, S., Hadsell, R., Ranzato, M., and Huang, F · 2006
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Divergent thinking as an indicator of creative potential
Runco, M. A. and Acar, S · 2012
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Diverse beam search: Decoding diverse solutions from neural sequence models
Vijayakumar, A. K., Cogswell, M., Selvaraju, R. R., Sun, Q., Lee, S., Crandall, D., and Batra, D · 2016
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Proximal policy optimization algorithms
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The curious case of neural text degeneration
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Flow network based generative models for non-iterative diverse candidate generation
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Training verifiers to solve math word problems
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Measuring mathematical problem solving with the math dataset
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Lora: Low-rank adaptation of large language models
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RiddleSense: Reasoning about riddle questions featuring linguistic creativity and commonsense knowledge
Lin, B. Y., Wu, Z., Yang, Y., Lee, D.-H., and Ren, X · 2021
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Constitutional ai: Harmlessness from ai feedback
Bai, Y., Kadavath, S., Kundu, S., Askell, A., Kernion, J., Jones, A., Chen, A., Goldie, A., Mirhoseini, A., McKinnon, C., et al · 2022
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Chen, W., Ma, X., Wang, X., and Cohen, W. W · 2022
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Bayesian structure learning with generative flow networks
Deleu, T., Góis, A., Emezue, C., Rankawat, M., Lacoste-Julien, S., Bauer, S., and Bengio, Y · 2022
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Biological sequence design with gflownets
Jain, M., Bengio, E., Hernandez-Garcia, A., Rector-Brooks, J., Dossou, B. F., Ekbote, C. A., Fu, J., Zhang, T., Kilgour, M., Zhang, D., et al · 2022
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Maieutic prompting: Logically consistent reasoning with recursive explanations
Jung, J., Qin, L., Welleck, S., Brahman, F., Bhagavatula, C., Bras, R. L., and Choi, Y · 2022
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Large language models are zero-shot reasoners
Kojima, T., Gu, S. S., Reid, M., Matsuo, Y., and Iwasawa, Y · 2022
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Lila: A unified benchmark for mathematical reasoning
Mishra, S., Finlayson, M., Lu, P., Tang, L., Welleck, S., Baral, C., Rajpurohit, T., Tafjord, O., Sabharwal, A., Clark, P., et al · 2022
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Training language models to follow instructions with human feedback
Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al · 2022
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Generative augmented flow networks
Pan, L., Zhang, D., Courville, A., Huang, L., and Bengio, Y · 2022
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Cold decoding: Energy-based constrained text generation with langevin dynamics
Qin, L., Welleck, S., Khashabi, D., and Choi, Y · 2022
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Language models are greedy reasoners: A systematic formal analysis of chain-of-thought
Saparov, A. and He, H · 2022
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Self-critiquing models for assisting human evaluators
Saunders, W., Yeh, C., Wu, J., Bills, S., Ouyang, L., Ward, J., and Leike, J · 2022
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Solving math word problems with process-and outcome-based feedback
Uesato, J., Kushman, N., Kumar, R., Song, F., Siegel, N., Wang, L., Creswell, A., Irving, G., and Higgins, I · 2022
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Self-consistency improves chain of thought reasoning in language models
Wang, X., Wei, J., Schuurmans, D., Le, Q., Chi, E., Narang, S., Chowdhery, A., and Zhou, D · 2022
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Chain-of-thought prompting elicits reasoning in large language models
Wei, J., Wang, X., Schuurmans, D., Bosma, M., Xia, F., Chi, E., Le, Q. V., Zhou, D., et al · 2022
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Star: Bootstrapping reasoning with reasoning
Zelikman, E., Wu, Y., Mu, J., and Goodman, N · 2022
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Generative flow networks for discrete probabilistic modeling
Zhang, D., Malkin, N., Liu, Z., Volokhova, A., Courville, A., and Bengio, Y · 2022
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Least-to-most prompting enables complex reasoning in large language models
Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al · 2022
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Solving math word problems via cooperative reasoning induced language models
Zhu, X., Wang, J., Zhang, L., Zhang, Y., Huang, Y., Gan, R., Zhang, J., and Yang, Y · 2022
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Achiam, J., Adler, S., Agarwal, S., Ahmad, L., Akkaya, I., Aleman, F. L., Almeida, D., Altenschmidt, J., Altman, S., Anadkat, S., et al · 2023
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Gflownet foundations
Bengio, Y., Lahlou, S., Deleu, T., Hu, E. J., Tiwari, M., and Bengio, E · 2023
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Chen, Y. and Mauch, L · 2023
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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., et al · 2023
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Generative flow networks: a markov chain perspective
Deleu, T. and Bengio, Y · 2023
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Everything of thoughts: Defying the law of penrose triangle for thought generation
Ding, R., Zhang, C., Wang, L., Xu, Y., Ma, M., Zhang, W., Qin, S., Rajmohan, S., Lin, Q., and Zhang, D · 2023
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Alphazero-like tree-search can guide large language model decoding and training
Feng, X., Wan, Z., Wen, M., Wen, Y., Zhang, W., and Wang, J · 2023
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Generative flow networks assisted biological sequence editing
Ghari, P. M., Tseng, A., Eraslan, G., Lopez, R., Biancalani, T., Scalia, G., and Hajiramezanali, E · 2023
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Joint bayesian inference of graphical structure and parameters with a single generative flow network
Deleu, T., Nishikawa-Toomey, M., Subramanian, J., Malkin, N., Charlin, L., and Bengio, Y · 2024
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Dubey, A., Jauhri, A., Pandey, A., Kadian, A., Al-Dahle, A., Letman, A., Mathur, A., Schelten, A., Yang, A., Fan, A., et al · 2024
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Hao, S., Gu, Y., Luo, H., Liu, T., Shao, X., Wang, X., Xie, S., Ma, H., Samavedhi, A., Gao, Q., et al · 2024
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Teaching large language models to reason with reinforcement learning
Havrilla, A., Du, Y., Raparthy, S. C., Nalmpantis, C., Dwivedi-Yu, J., Zhuravinskyi, M., Hambro, E., Sukhbaatar, S., and Raileanu, R · 2024
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Hao, S., Gu, Y., Ma, H., Hong, J. J., Wang, Z., Wang, D. Z., and Hu, Z · 2023
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Multi-fidelity active learning with gflownets
Hernandez-Garcia, A., Saxena, N., Jain, M., Liu, C.-H., and Bengio, Y · 2023
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Huang, S., Ma, S., Li, Y., Huang, M., Zou, W., Zhang, W., and Zheng, H.-T · 2023
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Learning energy decompositions for partial inference of gflownets
Jang, H., Kim, M., and Ahn, S · 2023
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BRAINTEASER: Lateral thinking puzzles for large language models
Jiang, Y., Ilievski, F., Ma, K., and Sourati, Z · 2023
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Kim, M., Yun, T., Bengio, E., Zhang, D., Bengio, Y., Ahn, S., and Park, J · 2023
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A theory of continuous generative flow networks
Lahlou, S., Deleu, T., Lemos, P., Zhang, D., Volokhova, A., Hernández-Garcıa, A., Ezzine, L. N., Bengio, Y., and Malkin, N · 2023
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Lightman, H., Kosaraju, V., Burda, Y., Edwards, H., Baker, B., Lee, T., Leike, J., Schulman, J., Sutskever, I., and Cobbe, K · 2023
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He, H., Chang, C., Xu, H., and Pan, L · 2024
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V-star: Training verifiers for self-taught reasoners
Hosseini, A., Yuan, X., Malkin, N., Courville, A., Sordoni, A., and Agarwal, R · 2024
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Creativity in ai: Progresses and challenges
Ismayilzada, M., Paul, D., Bosselut, A., and van der Plas, L · 2024
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Mmtom-qa: Multimodal theory of mind question answering
Jin, C., Wu, Y., Cao, J., Xiang, J., Kuo, Y.-L., Hu, Z., Ullman, T., Torralba, A., Tenenbaum, J. B., and Shu, T · 2024
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Llms can’t plan, but can help planning in llm-modulo frameworks
Kambhampati, S., Valmeekam, K., Guan, L., Stechly, K., Verma, M., Bhambri, S., Saldyt, L., and Murthy, A · 2024
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Rgfn: Synthesizable molecular generation using gflownets
Koziarski, M., Rekesh, A., Shevchuk, D., van der Sloot, A., Gaiński, P., Bengio, Y., Liu, C.-H., Tyers, M., and Batey, R. A · 2024
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Columbus: Evaluating cognitive lateral understanding through multiple-choice rebuses
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Training language models to self-correct via reinforcement learning
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Gdpo: Learning to directly align language models with diversity using gflownets
Kwon, O. J., Matsunaga, D. E., and Kim, K.-E · 2024
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Cell morphology-guided small molecule generation with gflownets
Lu, S. Z., Lu, Z., Hajiramezanali, E., Biancalani, T., Bengio, Y., Scalia, G., and Koziarski, M · 2024
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Reft: Reasoning with reinforced fine-tuning
Luong, T. Q., Zhang, X., Jie, Z., Sun, P., Jin, X., and Li, H · 2024
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Baking symmetry into gflownets
Ma, G., Bengio, E., Bengio, Y., and Zhang, D · 2024
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Self-refine: Iterative refinement with self-feedback
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Goal2flownet: Learning diverse policy covers using GFlownets for goal-conditioned RL, 2024
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Paraphrasus: A comprehensive benchmark for evaluating paraphrase detection models
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A thorough examination of decoding methods in the era of llms
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Latent logic tree extraction for event sequence explanation from llms
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Gflownet fine-tuning for diverse correct solutions in mathematical reasoning tasks
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Missed connections: Lateral thinking puzzles for large language models
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