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arXiv Computation and Language By Junrong Tang, Jianghui Wang, Hao Kong, Yang Lin, Yibo Yang

HyperRepair: Recovering Forgotten General Capabilities of Fine-Tuned Language Models

Read the original on arXiv Computation and Language →

HyperRepair is a framework that restores the general capabilities of fine‑tuned language models while keeping their domain specialization, using only inference time. It learns a hypernetwork from repairs on source models, which generates a Repairing Side Net (RSN) that selectively corrects the model based on activation fingerprints. Evaluated on two model families, 12 LoRA adaptation datasets, and 15 general benchmarks, HyperRepair retains 99.5% of fine‑tuned domain performance and 93.4% of the base model’s general performance.

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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