Open Access
Issue
SHS Web Conf.
Volume 231, 2026
7th International Symposium on Frontiers of Economics and Management Science (FEMS 2026)
Article Number 01012
Number of page(s) 5
DOI https://doi.org/10.1051/shsconf/202623101012
Published online 19 May 2026
  1. Chen Z, Chen W, Smiley C, et al. Finqa: A dataset of numerical reasoning over financial data[C]//Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. 2021: 3697–3711. [Google Scholar]
  2. Huang Y, Lv T, Cui L, et al. Layoutlmv3: Pretraining for document ai with unified text and image masking[C]//Proceedings of the 30th ACM international conference on multimedia. 2022: 4083–4091. [Google Scholar]
  3. Brown T, Mann B, Ryder N, et al. Language models are few-shot learners[J]. Advances in neural information processing systems, 2020, 33: 1877–1901. [Google Scholar]
  4. Li J, Li D, Savarese S, et al. Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models [C] //International conference on machine learning. PMLR, 2023: 19730–19742. [Google Scholar]
  5. Masry A, Do X L, Tan J Q, et al. Chartqa: A benchmark for question answering about charts with visual and logical reasoning[C]//Findings of the association for computational linguistics: ACL 2022. 2022: 2263–2279. [Google Scholar]
  6. Zhu F, Lei W, Huang Y, et al. TAT-QA: A question answering benchmark on a hybrid of tabular and textual content in finance[C]//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). 2021: 3277–3287. [Google Scholar]
  7. Liu F, Piccinino F, Krichene S, et al. Matcha: Enhancing visual language pretraining with math reasoning and chart derendering[C]//Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2023: 12756–12770. [Google Scholar]
  8. Lee K, Joshi M, Turc I R, et al. Pix2struct: Screenshot parsing as pretraining for visual language understanding[C]//International Conference on Machine Learning. PMLR, 2023: 18893–18912. [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.