most citedMitigating Training Imbalance in LLM Fine-Tuning via Selective Parameter Merging

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cs.CL2026

LocalSUG: City-Preference-Enhanced LLM for Query Suggestion in Local-Life Services

Jinwen Chen, Shiwen Zhang, Shuai Gong +6

In local-life service platforms, query suggestion reduces user effort by generating candidate queries from input prefixes. Traditional multi-stage systems rely heavily on historica…

cs.CL2024★ 1 cited

Mitigating Training Imbalance in LLM Fine-Tuning via Selective Parameter Merging

Yiming Ju, Ziyi Ni, Xingrun Xing +4

Supervised fine-tuning (SFT) is crucial for adapting Large Language Models (LLMs) to specific tasks. In this work, we demonstrate that the order of training data can lead to signif…