RepTran: Search-Based Repair of Transformer Models
To ensure the overall quality of AI-enabled software, not only traditional software components but also AI components need to be tested and repaired. Among AI components, Transformer models are increasingly integrated into software systems, which makes their misbehaviors critical. Although prior work in the software engineering community has proposed deep neural network (DNN) repair methods, most overlook Transformer-specific structures. We propose RepTran, a search-based repair method for Transformer models. It targets their feed-forward networks (FFNs), which play a central role in the architecture. RepTran identifies suspicious weights by combining two types of scores: a variance-based...
| Authors | Yuta Ishimoto, Paolo Arcaini, Fuyuki Ishikawa et al. |
|---|---|
| Primary category | cs.SE |
| Published | 13 July 2026 |
| Last updated at source | 14 July 2026 |
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