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Recent Large Reasoning Models significantly improve the reasoning ability of Large Language Models by learning to reason, exhibiting the promising performance in solving complex tasks.
G. Arreaga-Garcia and J. S. Morales, “Equations of motion of a relativistic charged particle with a curvature depending actions,” 2013
2013
Earlier work this paper cites.
A. Talmor, J. Herzig, N. Lourie, and J. Berant, “CommonsenseQA: A question answering challenge targeting commonsense knowledge,” in
2019
Earlier work this paper cites.
N. Reimers and I. Gurevych, “Sentence-bert: Sentence embeddings using siamese bert-networks,” 2019
2019
Earlier work this paper cites.
Z. Lu and Y. Chen, “Single image super resolution based on a modified u-net with mixed gradient loss,” 2019
2019
Earlier work this paper cites.
2021
Earlier work this paper cites.
D. Hendrycks, C. Burns, S. Basart, A. Zou, M. Mazeika, D. Song, and J. Steinhardt, “Measuring massive multitask language understanding,” 2021
2021
Earlier work this paper cites.
T. Gao, X. Yao, and D. Chen, “Simcse: Simple contrastive learning of sentence embeddings,”
2021
Earlier work this paper cites.
F. Shi, M. Suzgun, M. Freitag, X. Wang, S. Srivats, S. Vosoughi, H. W. Chung, Y. Tay, S. Ruder, D. Zhou, D. Das, and J. Wei, “Language models are multilingual chain-of-thought reasoners,” 2022
2022
Earlier work this paper cites.
D. Hendrycks, S. Basart, M. Mazeika, A. Zou, J. Kwon, M. Mostajabi, J. Steinhardt, and D. Song, “Scaling out-of-distribution detection for real-world settings,” 2022
2022
Earlier work this paper cites.
Z. Lu and Y. Chen, “Pyramid frequency network with spatial attention residual refinement module for monocular depth estimation,”
2022
Earlier work this paper cites.
S. Kadavath, T. Conerly, A. Askell, T. Henighan, D. Drain, E. Perez, N. Schiefer, Z. Hatfield-Dodds, N. DasSarma, E. Tran-Johnson, S. Johnston, S. El-Showk, A. Jones, N. Elhage, T. Hume, A. Chen, Y. Bai, S. Bowman, S. Fort, D. Ganguli, D. Hernandez, J. Jacobson, J. Kernion, S. Kravec, L. Lovitt, K. Ndousse, C. Olsson, S. Ringer, D. Amodei, T. Brown, J. Clark, N. Joseph, B. Mann, S. McCandlish, C. Olah, and J. Kaplan, “Language models (mostly) know what they know,” 2022
2022
Earlier work this paper cites.
L. Ouyang, J. Wu, X. Jiang, D. Almeida, C. L. Wainwright, P. Mishkin, C. Zhang, S. Agarwal, K. Slama, A. Ray, J. Schulman, J. Hilton, F. Kelton, L. Miller, M. Simens, A. Askell, P. Welinder, P. Christiano, J. Leike, and R. Lowe, “Training language models to follow instructions with human feedback,” 2022
2022
Earlier work this paper cites.
S. Lin, J. Hilton, and O. Evans, “Teaching models to express their uncertainty in words,” 2022
2022
Earlier work this paper cites.
W. Chen, M. Yin, M. Ku, P. Lu, Y. Wan, X. Ma, J. Xu, X. Wang, and T. Xia, “Theoremqa: A theorem-driven question answering dataset,” 2023
2023
Earlier work this paper cites.
M. Turpin, J. Michael, E. Perez, and S. R. Bowman, “Language models don’t always say what they think: Unfaithful explanations in chain-of-thought prompting,” 2023
2023
Earlier work this paper cites.
Y. Li, Z. Lin, S. Zhang, Q. Fu, B. Chen, J.-G. Lou, and W. Chen, “Making large language models better reasoners with step-aware verifier,” 2023
2023
Earlier work this paper cites.
A. Shih, D. Sadigh, and S. Ermon, “Long horizon temperature scaling,” 2023
2023
Earlier work this paper cites.
C. Si, Z. Gan, Z. Yang, S. Wang, J. Wang, J. Boyd-Graber, and L. Wang, “Prompting gpt-3 to be reliable,” 2023
2023
Earlier work this paper cites.
L. Kuhn, Y. Gal, and S. Farquhar, “Semantic uncertainty: Linguistic invariances for uncertainty estimation in natural language generation,” 2023
2023
Earlier work this paper cites.
Z. Lu and Y. Chen, “Joint self-supervised depth and optical flow estimation towards dynamic objects,”
2023
Earlier work this paper cites.
P. Jing, K. Cui, W. Guan, L. Nie, and Y. Su, “Category-aware multimodal attention network for fashion compatibility modeling,”
2023
Earlier work this paper cites.
S. Lu, Y. Liu, and A. W.-K. Kong, “Tf-icon: Diffusion-based training-free cross-domain image composition,” in
2023
Earlier work this paper cites.
W. Ye, C. Qian, X. An, X. Yan, and G. Carle, “Advancing federated learning in 6g: A trusted architecture with graph-based analysis,” 2023
2023
Earlier work this paper cites.
W. Ye, X. An, X. Yan, and G. Carle, “Trustworthy federated learning via decentralized consensus under communication constraints,” in
2023
Earlier work this paper cites.
W. Huang, G. Wan, M. Ye, and B. Du, “Federated graph semantic and structural learning,” in
2023
Earlier work this paper cites.
H. Chen, Y. Zhang, D. Krompass, J. Gu, and V. Tresp, “Feddat: An approach for foundation model finetuning in multi-modal heterogeneous federated learning,” 2023
2023
Earlier work this paper cites.
G. Zhang, J. Bi, J. Gu, Y. Chen, and V. Tresp, “Spot! revisiting video-language models for event understanding,” 2023
2023
Earlier work this paper cites.
J. Wei, X. Wang, D. Schuurmans, M. Bosma, B. Ichter, F. Xia, E. Chi, Q. Le, and D. Zhou, “Chain-of-thought prompting elicits reasoning in large language models,” 2023
2023
Earlier work this paper cites.
Z. Zhang, H. Yang, B. Ma, D. Rügamer, and E. Nie, “Baby’s cothought: Leveraging large language models for enhanced reasoning in compact models,” 2023
2023
Earlier work this paper cites.
K. Tian, E. Mitchell, A. Zhou, A. Sharma, R. Rafailov, H. Yao, C. Finn, and C. D. Manning, “Just ask for calibration: Strategies for eliciting calibrated confidence scores from language models fine-tuned with human feedback,” 2023
2023
Earlier work this paper cites.
P. Manakul, A. Liusie, and M. J. F. Gales, “Selfcheckgpt: Zero-resource black-box hallucination detection for generative large language models,” 2023
2023
Earlier work this paper cites.
Q. Lyu, S. Havaldar, A. Stein, L. Zhang, D. Rao, E. Wong, M. Apidianaki, and C. Callison-Burch, “Faithful chain-of-thought reasoning,” 2023
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