HPAA uses typographic manipulations to create text that humans flag as harmful at 86%+ rates while LLM moderation systems detect it below 1% with only three queries.
Can ll ms recognize toxic- ity? structured toxicity investigation framework and sema ntic-based metric
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
roles
background 1polarities
support 1representative citing papers
Proposes a five-bucket taxonomy of LLM harms and calls for dynamic auditing, but the systematic review behind it is not reproducible and contains mismatched citations.
A literature review that categorizes bias in LLMs, surveys evaluation and mitigation techniques, and discusses ethical implications.
citing papers explorer
-
What the Eyes See, the LLMs Miss: Exploiting Human Perception for Adversarial Text Attacks
HPAA uses typographic manipulations to create text that humans flag as harmful at 86%+ rates while LLM moderation systems detect it below 1% with only three queries.
-
LLM Harms: A Taxonomy and Discussion
Proposes a five-bucket taxonomy of LLM harms and calls for dynamic auditing, but the systematic review behind it is not reproducible and contains mismatched citations.
-
Bias in Large Language Models: Origin, Evaluation, and Mitigation
A literature review that categorizes bias in LLMs, surveys evaluation and mitigation techniques, and discusses ethical implications.