3 citations · 5 across the 17 of their papers we have counts for
6 papers · 1 filter
Introspecting Alignment Shifts Beyond Behaviors Implanted Through Fine-Tuning
Kotaro Yoshida, Laura Gomezjurado Gonzalez, Yukinori Yamamoto +3
Fine-tuning enables a source model to acquire desired capabilities and behaviors in a target domain while retaining much of its general-purpose competence. However, this adaptation…
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…
Disentangling Likes and Dislikes in Personalized Generative Explainable Recommendation
Ryotaro Shimizu, Takashi Wada, Yu Wang +9
Recent research on explainable recommendation generally frames the task as a standard text generation problem, and evaluates models simply based on the textual similarity between t…
Explaining Black-box Model Predictions via Two-level Nested Feature Attributions with Consistency Property
Yuya Yoshikawa, Masanari Kimura, Ryotaro Shimizu +1
Techniques that explain the predictions of black-box machine learning models are crucial to make the models transparent, thereby increasing trust in AI systems. The input features…
On permutation-invariant neural networks
Masanari Kimura, Ryotaro Shimizu, Yuki Hirakawa +2
Conventional machine learning algorithms have traditionally been designed under the assumption that input data follows a vector-based format, with an emphasis on vector-centric par…