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New SMMD training method enhances numerical accuracy in LLMs

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a new training objective called Smooth Maximum Mean Discrepancy (SMMD) to improve the numerical precision of large language models (LLMs). Standard cross-entropy training treats numerical tokens as categories, ignoring their inherent value structure. SMMD addresses this by incorporating value-distance kernels and graph-based smoothness, aligning predicted distributions with target values and encouraging local consistency. Evaluations across various LLM and vision-language model backbones on tasks like mathematical reasoning and chart question answering show SMMD consistently outperforms existing methods. AI

IMPACT This new training objective could lead to more reliable LLMs for tasks requiring numerical precision, impacting fields like scientific research and financial analysis.

RANK_REASON The cluster contains a research paper detailing a new method for improving LLM performance.

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New SMMD training method enhances numerical accuracy in LLMs

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The cluster contains a research paper detailing a new method for improving LLM performance.
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COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Zhuo Zuo, Li Yue, Wenhao Zheng, Chenpeng Wang, Xianggen Liu ·

    Enhancing Numerical Prediction in LLMs via Smooth MMD Alignment

    arXiv:2606.27731v1 Announce Type: cross Abstract: Despite their strong general capabilities, large language models (LLMs) often remain unreliable when outputs must be numerically precise. A key reason is the training objective: standard cross-entropy treats numeric tokens as unst…

  2. arXiv cs.CL TIER_1 English(EN) · Xianggen Liu ·

    Enhancing Numerical Prediction in LLMs via Smooth MMD Alignment

    Despite their strong general capabilities, large language models (LLMs) often remain unreliable when outputs must be numerically precise. A key reason is the training objective: standard cross-entropy treats numeric tokens as unstructured categories and ignores the metric structu…

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