Summary of Interpreting Key Mechanisms Of Factual Recall in Transformer-based Language Models, by Ang Lv et al.
Interpreting Key Mechanisms of Factual Recall in Transformer-Based Language Modelsby Ang Lv, Yuhan Chen, Kaiyi…
Interpreting Key Mechanisms of Factual Recall in Transformer-Based Language Modelsby Ang Lv, Yuhan Chen, Kaiyi…
Evaluating Large Language Models for Health-Related Text Classification Tasks with Public Social Media Databy Yuting…
PLOT-TAL – Prompt Learning with Optimal Transport for Few-Shot Temporal Action Localizationby Edward Fish, Jon…
SingularTrajectory: Universal Trajectory Predictor Using Diffusion Modelby Inhwan Bae, Young-Jae Park, Hae-Gon JeonFirst submitted to…
Generative Multi-modal Models are Good Class-Incremental Learnersby Xusheng Cao, Haori Lu, Linlan Huang, Xialei Liu,…
Few-Shot Recalibration of Language Modelsby Xiang Lisa Li, Urvashi Khandelwal, Kelvin GuuFirst submitted to arxiv…
Using Domain Knowledge to Guide Dialog Structure Induction via Neural Probabilistic Soft Logicby Connor Pryor,…
Dual Memory Networks: A Versatile Adaptation Approach for Vision-Language Modelsby Yabin Zhang, Wenjie Zhu, Hui…
Deep Support Vectorsby Junhoo Lee, Hyunho Lee, Kyomin Hwang, Nojun KwakFirst submitted to arxiv on:…
Exploring the Generalization of Cancer Clinical Trial Eligibility Classifiers Across Diseasesby Yumeng Yang, Ashley Gilliam,…