arXiv:2601.08146v3 Announce Type: replace-cross Abstract: Existing circuit discovery methods rely on templated tasks with clean counterfactuals, limiting their use on diverse natural text. We adapt Contextual Decomposition for Transformers (CD-T) for unstructured settings via label-balanced activation means and task-directional relevance scoring, enabling counterfactual-free circuit discovery. We leverage these circuits for Circuit-Targeted Supervised Fine-Tuning (CT-SFT), restricting parameter updates to task-relevant heads and LayerNorm. Experiments on NusaX cross-lingual sentiment transfer
The proliferation of large language models and the increasing demand for efficient, adaptable AI in diverse linguistic and task environments necessitates new methods for targeted adaptation and understanding of model mechanics.
This research introduces a novel, counterfactual-free approach to circuit discovery in Transformers, enabling more faithful and efficient adaptation of AI models, particularly in low-resource settings, by focusing on task-relevant components.
AI model adaptation moves beyond traditional transfer accuracy metrics to methods that offer greater transparency and efficiency in modifying model behavior for specific tasks and languages.
- · AI researchers and developers
- · Companies using AI in low-resource languages
- · Sectors requiring efficient model fine-tuning
- · Approaches relying solely on black-box transfer learning
- · Less efficient full-model fine-tuning methods
More efficient and interpretable adaptation of large language models for various downstream tasks and languages, reducing computational overhead.
Accelerated development of specialized AI applications for underrepresented languages and domains due to lower resource requirements for adaptation.
Potential for new AI services and products that leverage highly customized and efficient smaller models derived from larger foundational models.
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