Decoding Compressed Trust: Scrutinizing the Trustworthiness of Efficient LLMs Under Compression

arXiv:2403.15447v1 Announce Type: new
Abstract: Compressing high-capability Large Language Models (LLMs) has emerged as a favored strategy for resource-efficient inferences. While state-of-the-art (SoTA) compression methods boast impressive advancements in preserving benign task performance, the potential risks of compression in terms of safety and trustworthiness have been largely neglected. This study conducts the first, thorough evaluation of three (3) leading LLMs using five (5) SoTA compression techniques across eight (8) trustworthiness dimensions. Our experiments highlight the intricate interplay between compression and …

arxiv compression cs.ai cs.cl decoding llms trust type