Papers with language-agnostic or modality-agnostic classifier
MULTIGUARD: An Efficient Approach for AI Safety Moderation Across Languages and Modalities (2025.emnlp-main)
Copied to clipboard
Sahil Verma, Keegan Hines, Jeff Bilmes, Charlotte Siska, Luke Zettlemoyer, Hila Gonen, Chandan Singh
| Challenge: | Existing approaches to detect harmful queries to large language models are fallible and vulnerable to attacks that exploit mismatched generalization of model capabilities. |
| Approach: | They propose an approach to detect harmful queries to large language models (LLMs) OMNIGUARD identifies internal representations of an LLM/MLLM that are aligned across languages or modalities and builds a language-agnostic or modality-adic classifier for detecting harmful prompts. |
| Outcome: | OMNIGUARD improves harmful prompt classification accuracy by 11.57% over the strongest baseline in a multilingual setting, by 20.44% for image-based prompts, and sets a new SOTA for audio-based ones. |