Advancing Sequential Numerical Prediction in Autoregressive Models

• URL: http://arxiv.org/abs/2505.13077v2
• Date: Wed, 28 May 2025 10:48:01 GMT
• Title: Advancing Sequential Numerical Prediction in Autoregressive Models
• Authors: Xiang Fei, Jinghui Lu, Qi Sun, Hao Feng, Yanjie Wang, Wei Shi, An-Lan Wang, Jingqun Tang, Can Huang,
• Abstract summary: This paper introduces Numerical Token Integrity Loss (NTIL) to address this gap.<n>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.
• Score: 26.759068834681738
• License: http://creativecommons.org/publicdomain/zero/1.0/
• 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.

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