@inproceedings{fei-etal-2025-advancing,
title = "Advancing Sequential Numerical Prediction in Autoregressive Models",
author = "Fei, Xiang and
Lu, Jinghui and
Sun, Qi and
Feng, Hao and
Wang, Yanjie and
Shi, Wei and
Wang, An-Lan and
Tang, Jingqun and
Huang, Can",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-short.44/",
doi = "10.18653/v1/2025.acl-short.44",
pages = "562--574",
ISBN = "979-8-89176-252-7",
abstract = "Autoregressive models have become the de facto choice for sequence generation tasks, but standard approaches treat digits as independent tokens and apply cross-entropy loss, overlooking the coherent structure of numerical sequences. This paper introduces $\textit{\textbf{N}umerical \textbf{T}oken \textbf{I}ntegrity \textbf{Loss} (NTIL)}$ to address this gap. NTIL operates at two levels: (1) token-level, where it extends the Earth Mover{'}s Distance (EMD) to preserve ordinal relationships between numerical values, and (2) sequence-level, where it penalizes the overall discrepancy between the predicted and actual sequences. This dual approach improves numerical prediction and integrates effectively with LLMs/MLLMs. Extensive experiments show significant performance improvements with NTIL."
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<abstract>Autoregressive models have become the de facto choice for sequence generation tasks, but standard approaches treat digits as independent tokens and apply cross-entropy loss, overlooking the coherent structure of numerical sequences. This paper introduces Numerical Token Integrity Loss (NTIL) to address this gap. NTIL operates at two levels: (1) token-level, where it extends the Earth Mover’s Distance (EMD) to preserve ordinal relationships between numerical values, and (2) sequence-level, where it penalizes the overall discrepancy between the predicted and actual sequences. This dual approach improves numerical prediction and integrates effectively with LLMs/MLLMs. Extensive experiments show significant performance improvements with NTIL.</abstract>
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%0 Conference Proceedings
%T Advancing Sequential Numerical Prediction in Autoregressive Models
%A Fei, Xiang
%A Lu, Jinghui
%A Sun, Qi
%A Feng, Hao
%A Wang, Yanjie
%A Shi, Wei
%A Wang, An-Lan
%A Tang, Jingqun
%A Huang, Can
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-252-7
%F fei-etal-2025-advancing
%X Autoregressive models have become the de facto choice for sequence generation tasks, but standard approaches treat digits as independent tokens and apply cross-entropy loss, overlooking the coherent structure of numerical sequences. This paper introduces Numerical Token Integrity Loss (NTIL) to address this gap. NTIL operates at two levels: (1) token-level, where it extends the Earth Mover’s Distance (EMD) to preserve ordinal relationships between numerical values, and (2) sequence-level, where it penalizes the overall discrepancy between the predicted and actual sequences. This dual approach improves numerical prediction and integrates effectively with LLMs/MLLMs. Extensive experiments show significant performance improvements with NTIL.
%R 10.18653/v1/2025.acl-short.44
%U https://aclanthology.org/2025.acl-short.44/
%U https://doi.org/10.18653/v1/2025.acl-short.44
%P 562-574
Markdown (Informal)
[Advancing Sequential Numerical Prediction in Autoregressive Models](https://aclanthology.org/2025.acl-short.44/) (Fei et al., ACL 2025)
ACL
- Xiang Fei, Jinghui Lu, Qi Sun, Hao Feng, Yanjie Wang, Wei Shi, An-Lan Wang, Jingqun Tang, and Can Huang. 2025. Advancing Sequential Numerical Prediction in Autoregressive Models. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 562–574, Vienna, Austria. Association for Computational Linguistics.