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cs.CL2024
Approximate Attributions for Off-the-Shelf Siamese Transformers
Lucas Möller, Dmitry Nikolaev, Sebastian Padó
Siamese encoders such as sentence transformers are among the least understood deep models. Established attribution methods cannot tackle this model class since it compares two inpu…
cs.CL2023
An Attribution Method for Siamese Encoders
Lucas Möller, Dmitry Nikolaev, Sebastian Padó
Despite the success of Siamese encoder models such as sentence transformers (ST), little is known about the aspects of inputs they pay attention to. A barrier is that their predict…