Robust Conversational Agents against Imperceptible Toxicity Triggers

Anonymous

Paper Link: https://openreview.net/forum?id=8DFFnNAdCmN

Paper Type: Long paper (up to eight pages of content + unlimited references and appendices)

Abstract:

Warning: this paper contains content that maybe offensive or upsetting.
Recent research in Natural Language Processing (NLP) has advanced the development of various toxicity detection models with the intention of identifying and mitigating toxic language from existing systems. Despite the abundance of research in this area, less attention has been given to adversarial attacks that force the system to generate toxic language and the defense against them.
Existing work to generate such attacks is either based on human-generated attacks which is costly and not scalable or, in case of automatic attacks, the attack vector does not conform to human-like language, which can be detected using a language model loss. In this work, we propose attacks against conversational agents that are imperceptible, i.e., they fit the conversation in terms of coherency, relevancy, and fluency, while they are effective and scalable, i.e., they can automatically trigger the system into generating toxic language. We then propose a defense mechanism against such attacks which not only mitigates the attack but also attempts to maintain the conversational flow. Through automatic and human evaluations, we show that our defense is effective at avoiding toxic language generation even against imperceptible toxicity triggers while the generated language fits the conversation in terms of coherency and relevancy. Lastly, we establish the generalizability of such a defense mechanism on language generation models beyond conversational agents.

Presentation Mode: This paper will be presented virtually

Virtual Presentation Timezone: UTC-8

Copyright Consent Signature (type Name Or NA If Not Transferrable): Ninareh Mehrabi

Copyright Consent Name And Address: University of Southern California - Information Sciences Institute. 4676 Admiralty Way, Marina Del Rey, CA 90292