arXiv Computation and Language September 24, 2026 By Md Tahmid Rahman Laskar, Xue-Yong Fu, Shashi Bhushan TN

Can One Adapted Model Do It All? Fine-Tuning Strategy Selection for Customer Support LLMs

Read the original on arXiv Computation and Language →

The study evaluates whether a single large language model (LLM) can handle multiple customer‑support tasks or if separate specialist models are preferable. Using 13 models from five families and 200+ checkpoints across eight datasets, the authors find that multi‑task full fine‑tuning consistently outperforms other strategies. They also show that sequential LoRA and model merging can preserve earlier skills and improve off‑task robustness, offering practical guidelines for real‑world deployment.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.

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