Open Access
| Issue |
ITM Web Conf.
Volume 80, 2025
2025 2nd International Conference on Advanced Computer Applications and Artificial Intelligence (ACAAI 2025)
|
|
|---|---|---|
| Article Number | 01030 | |
| Number of page(s) | 7 | |
| Section | Machine Learning & Deep Learning Algorithms | |
| DOI | https://doi.org/10.1051/itmconf/20258001030 | |
| Published online | 16 December 2025 | |
- K. Cobbe, et al., Training verifiers to solve math word problems (GSM8K), arXiv preprint arXiv:2110.14168 (2021) [Google Scholar]
- D. Hendrycks, et al., Measuring mathematical problem solving with the MATH dataset, arXiv preprint arXiv:2103.03874 (2021) [Google Scholar]
- J. Wei, et al., Chain-of-thought prompting elicits reasoning in large language models, arXiv preprint arXiv:2201.11903 (2022) [Google Scholar]
- X. Wang, et al., Self-consistency improves chain of thought reasoning in language models, Int. Conf. on Learning Representations (ICLR) (2023) arXiv:2203.11171 [Google Scholar]
- D. Zhou, et al., Least-to-most prompting enables complex reasoning in large language models, arXiv preprint arXiv:2205.10625 (2022) [Google Scholar]
- S. Yao, et al., Tree of thoughts: deliberate problem solving with large language models, arXiv preprint arXiv:2305.10601 (2023) [Google Scholar]
- L. Gao, et al., PAL: Program-aided language models, Int. Conf. on Machine Learning (ICML) (2023) arXiv:2211.10435 [Google Scholar]
- T. Schick, et al., Toolformer: Language models can teach themselves to use tools, Advances in Neural Information Processing Systems (NeurIPS) (2023) arXiv:2302.04761 [Google Scholar]
- A. Lewkowycz, et al., Solving quantitative reasoning problems with language models (Minerva), Advances in Neural Information Processing Systems (NeurIPS) (2022) arXiv:2206.14858 [Google Scholar]
- Z. Azerbayev, et al., Llemma: An open language model for mathematics, arXiv preprint arXiv:2310.10631 (2023) [Google Scholar]
- T.B. Brown, et al., Language models are few-shot learners, Advances in Neural Information Processing Systems (NeurIPS) (2020) [Google Scholar]
- P. Liu, et al., Toolformer: Language models can teach themselves to use tools, Int. Conf. on Machine Learning (ICML) (2023) [Google Scholar]
- J. Lee, et al., Program-aided language models (PAL) for precise computation, Conf. on Empirical Methods in Natural Language Processing (EMNLP) (2021) [Google Scholar]
- A. Shinn, et al., Minerva: Towards a more math-aware large language model, Int. Conf. on Learning Representations (ICLR) (2022) [Google Scholar]
- A. Shinn, et al., Vision Mamba: Combining visual and linguistic reasoning for mathematical problems, IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) (2023) [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.