4 papers
DisTaC: Conditioning Task Vectors via Distillation for Robust Model Merging
Kotaro Yoshida, Yuji Naraki, Takafumi Horie +2
Model merging has emerged as an efficient and flexible paradigm for multi-task learning, with numerous methods being proposed in recent years. However, these state-of-the-art techn…
On Fairness of Task Arithmetic: The Role of Task Vectors
Hiroki Naganuma, Kotaro Yoshida, Laura Gomezjurado Gonzalez +3
Model editing techniques, particularly task arithmetic with task vectors, offer an efficient alternative to full fine-tuning by enabling direct parameter updates through simple ari…
Generalization Measures under Controlled Covariate Shift: A Regime-Aware Benchmark
Sora Nakai, Youssef Fadhloun, Kacem Mathlouthi +4
Predicting generalization from quantities available before target-test evaluation remains a central challenge in deep learning. The systematic benchmark of Jiang et al. (2020) eval…
Augmenting NER Datasets with LLMs: Towards Automated and Refined Annotation
Yuji Naraki, Ryosuke Yamaki, Yoshikazu Ikeda +4
In the field of Natural Language Processing (NLP), Named Entity Recognition (NER) is recognized as a critical technology, employed across a wide array of applications. Traditional…