3 papers
cs.CL2025
Exploring Task Performance with Interpretable Models via Sparse Auto-Encoders
Shun Wang, Tyler Loakman, Youbo Lei +5
Large Language Models (LLMs) are traditionally viewed as black-box algorithms, therefore reducing trustworthiness and obscuring potential approaches to increasing performance on do…
cs.CV2023
MCAD: Multi-teacher Cross-modal Alignment Distillation for efficient image-text retrieval
Youbo Lei, Feifei He, Chen Chen +4
Due to the success of large-scale visual-language pretraining (VLP) models and the widespread use of image-text retrieval in industry areas, it is now critically necessary to reduc…
cs.CL2023
Prompt Space Optimizing Few-shot Reasoning Success with Large Language Models
Fobo Shi, Peijun Qing, Dong Yang +5
Prompt engineering is an essential technique for enhancing the abilities of large language models (LLMs) by providing explicit and specific instructions. It enables LLMs to excel i…