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Code generation, symbolic math reasoning, and other tasks require LLMs to produce outputs that are both syntactically and semantically correct.
Z3: an efficient smt solver
De Moura, L. and Bjørner, N · 2008
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Computational Complexity: A Modern Approach
Arora, S. and Barak, B · 2009
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Prover9 and mace4
McCune, W · 2010
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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., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., and Chintala, S · 2019
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Theoretical limitations of self-attention in neural sequence models
Hahn, M · 2020
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Transformers: State-of-the-art natural language processing
Wolf, T., Debut, L., Sanh, V., Chaumond, J., Delangue, C., Moi, A., Cistac, P., Rault, T., Louf, R., Funtowicz, M., Davison, J., Shleifer, S., von Platen, P., Ma, C., Jernite, Y., Plu, J., Xu, C., Le Scao, T., Gugger, S., Drame, M., Lhoest, Q., and Rush, A · 2020
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Formal language recognition by hard attention transformers: Perspectives from circuit complexity
Hao, Y., Angluin, D., and Frank, R · 2022
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Saturated transformers are constant-depth threshold circuits
Merrill, W., Sabharwal, A., and Smith, N. A · 2022
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Synchromesh: Reliable code generation from pre-trained language models
Poesia, G., Polozov, A., Le, V., Tiwari, A., Soares, G., Meek, C., and Gulwani, S · 2022
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Chain-of-thought prompting elicits reasoning in large language models
Wei, J., Wang, X., Schuurmans, D., Bosma, M., Ichter, B., Xia, F., Chi, E. H., Le, Q. V., and Zhou, D · 2022
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Autoformalization with large language models
Wu, Y., Jiang, A. Q., Li, W., Rabe, M. N., Staats, C. E., Jamnik, M., and Szegedy, C · 2022
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Program of thoughts prompting: Disentangling computation from reasoning for numerical reasoning tasks
Chen, W., Ma, X., Wang, X., and Cohen, W. W · 2023
Cited alongside, same era.
Guidance-ai/guidance: A guidance language for controlling large language models., 2023
Lundberg, S., Ribeiro, M. T. A. p., and et. al · 2023
Cited alongside, same era.
The parallelism tradeoff: Limitations of log-precision transformers
Merrill, W. and Sabharwal, A · 2023
Cited alongside, same era.
Linc: A neurosymbolic approach for logical reasoning by combining language models with first-order logic provers
Olausson, T., Gu, A., Lipkin, B., Zhang, C., Solar-Lezama, A., Tenenbaum, J., and Levy, R · 2023
Cited alongside, same era.
Pan, L., Albalak, A., Wang, X., and Wang, W. Y · 2023
Cited alongside, same era.
Chain of thought empowers transformers to solve inherently serial problems
Li, Z., Liu, H., Zhou, D., and Ma, T · 2024
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The llama 3 herd of models, 2024
Llama · 2024
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The expressive power of transformers with chain of thought
Merrill, W. and Sabharwal, A · 2024
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Gsm-symbolic: Understanding the limitations of mathematical reasoning in large language models, 2024
Mirzadeh, I., Alizadeh, K., Shahrokhi, H., Tuzel, O., Bengio, S., and Farajtabar, M · 2024
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Opneai tools, 2024
OpenAI · 2024
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Efficient guided generation for large language models, 2023
Willard, B. T. and Louf, R · 2023
Cited alongside, same era.
Wolfram alpha, 2024
wolfram · 2023
Cited alongside, same era.
Leandojo: Theorem proving with retrieval-augmented language models, 2023
Yang, K., Swope, A. M., Gu, A., Chalamala, R., Song, P., Yu, S., Godil, S., Prenger, R., and Anandkumar, A · 2023
Cited alongside, same era.
Guiding llms the right way: Fast, non-invasive constrained generation, 2024
Beurer-Kellner, L., Fischer, M., and Vechev, M · 2024
Cited alongside, same era.
Folio: Natural language reasoning with first-order logic, 2024
Han, S., Schoelkopf, H., Zhao, Y., Qi, Z., Riddell, M., Zhou, W., Coady, J., Peng, D., Qiao, Y., Benson, L., Sun, L., Wardle-Solano, A., Szabo, H., Zubova, E., Burtell, M., Fan, J., Liu, Y., Wong, B., Sailor, M., Ni, A., Nan, L., Kasai, J., Yu, T., Zhang, R., Fabbri, A. R., Kryscinski, W., Yavuz, S., Liu, Y., Lin, X. V., Joty, S., Zhou, Y., Xiong, C., Ying, R., Cohan, A., and Radev, D · 2024
Cited alongside, same era.
A survey on large language models for code generation, 2024
Jiang, J., Wang, F., Shen, J., Kim, S., and Kim, S · 2024
Cited alongside, same era.
Melcer, D., Fulton, N., Gouda, S. K., and Qian, H
Cited in the paper.
Park, K., Wang, J., Berg-Kirkpatrick, T., Polikarpova, N., and D’Antoni, L · 2024
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Qwen2.5: A party of foundation models, September 2024
Qwen · 2024
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What formal languages can transformers express? a survey
Strobl, L., Merrill, W., Weiss, G., Chiang, D., and Angluin, D · 2024
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Let me speak freely? a study on the impact of format restrictions on large language model performance
Tam, Z. R., Wu, C.-K., Tsai, Y.-L., Lin, C.-Y., Lee, H.-y., and Chen, Y.-N · 2024
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Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning, 2025
DeepSeek-AI, Guo, D., Yang, D., Zhang, H., Song, J., Zhang, R., Xu, R., Zhu, Q., Ma, S., Wang, P., Bi, X., Zhang, X., Yu, X., Wu, Y., Wu, Z. F., Gou, Z., Shao, Z., Li, Z., Gao, Z., Liu, A., Xue, B., Wang, B., Wu, B., Feng, B., Lu, C., Zhao, C., Deng, C., Zhang, C., Ruan, C., Dai, D., Chen, D., Ji, D., Li, E., Lin, F., Dai, F., Luo, F., Hao, G., Chen, G., Li, G., Zhang, H., Bao, H., Xu, H., Wang, H., Ding, H., Xin, H., Gao, H., Qu, H., Li, H., Guo, J., Li, J., Wang, J., Chen, J., Yuan, J., Qiu, J., Li, J., Cai, J. L., Ni, J., Liang, J., Chen, J., Dong, K., Hu, K., Gao, K., Guan, K., Huang, K., Yu, K., Wang, L., Zhang, L., Zhao, L., Wang, L., Zhang, L., Xu, L., Xia, L., Zhang, M., Zhang, M., Tang, M., Li, M., Wang, M., Li, M., Tian, N., Huang, P., Zhang, P., Wang, Q., Chen, Q., Du, Q., Ge, R., Zhang, R., Pan, R., Wang, R., Chen, R. J., Jin, R. L., Chen, R., Lu, S., Zhou, S., Chen, S., Ye, S., Wang, S., Yu, S., Zhou, S., Pan, S., Li, S. S., Zhou, S., Wu, S., Ye, S., Yun, T., Pei, T., Sun, T., Wang, T., Zeng, W., Zhao, W., Liu, W., Liang, W., Gao, W., Yu, W., Zhang, W., Xiao, W. L., An, W., Liu, X., Wang, X., Chen, X., Nie, X., Cheng, X., Liu, X., Xie, X., Liu, X., Yang, X., Li, X., Su, X., Lin, X., Li, X. Q., Jin, X., Shen, X., Chen, X., Sun, X., Wang, X., Song, X., Zhou, X., Wang, X., Shan, X., Li, Y. K., Wang, Y. Q., Wei, Y. X., Zhang, Y., Xu, Y., Li, Y., Zhao, Y., Sun, Y., Wang, Y., Yu, Y., Zhang, Y., Shi, Y., Xiong, Y., He, Y., Piao, Y., Wang, Y., Tan, Y., Ma, Y., Liu, Y., Guo, Y., Ou, Y., Wang, Y., Gong, Y., Zou, Y., He, Y., Xiong, Y., Luo, Y., You, Y., Liu, Y., Zhou, Y., Zhu, Y. X., Xu, Y., Huang, Y., Li, Y., Zheng, Y., Zhu, Y., Ma, Y., Tang, Y., Zha, Y., Yan, Y., Ren, Z. Z., Ren, Z., Sha, Z., Fu, Z., Xu, Z., Xie, Z., Zhang, Z., Hao, Z., Ma, Z., Yan, Z., Wu, Z., Gu, Z., Zhu, Z., Liu, Z., Li, Z., Xie, Z., Song, Z., Pan, Z., Huang, Z., Xu, Z., Zhang, Z., and Zhang, Z · 2025
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Qwq-32b: Embracing the power of reinforcement learning, March 2025
Team, Q · 2025
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