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Ling-Dao Sha

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#artificial intelligence Preprint Oct 2026

MoMaTune: Model, Match, Then Tune Language Models for Numerical Records

Despite remarkable progress in language understanding and generation, decoder language models continue to exhibit striking failures on numerical data. Numerical examples are typically serialized as text by concatenating feature names with their values (e.g., fat:3.4), while conventional task adaptation applies loss onl...

Weimin Lyu, Jing-Fen Zhang, Ling-Dao Sha et al. · 0 citations
#machine learning Preprint Sep 2026

TEFM: Token-Efficient Faithful Modeling for Structured Data

In this paper, we solve two fundamental obstacles in applying LLMs to critical domains: token efficiency and faithfulness. To address both constraints jointly, we present TEFM (Token-Efficient Faithful Modeling), a framework designed for structured data analysis in critical domains. TEFM achieves token efficiency by co...

Zhi-Chao Hou, Ling-Dao Sha, Xue-Yu Mao et al. · 2 citations

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