A participatory red-teaming project in the Global South created the PLACES dataset of 26k T2I failure examples that reveal unique cultural and linguistic harms missed by existing safety frameworks.
emnlp-main.307/
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
ExPerT infers query-specific user expertise from semantic text and keystroke dynamics via LLM prompting to adapt response generation, cutting inference error 65.7% and raising satisfaction 17.52% in a 40-participant study.
Obj-Disco decomposes LLM alignment reward signals into sparse weighted combinations of interpretable natural language objectives via iterative analysis of behavioral changes across checkpoints, capturing over 90% of observed reward behavior.
citing papers explorer
-
Going PLACES: Participatory Localized Red Teaming for Text-to-Image Safety in the Global South
A participatory red-teaming project in the Global South created the PLACES dataset of 26k T2I failure examples that reveal unique cultural and linguistic harms missed by existing safety frameworks.
-
ExPerT: Personalizing LLM Responses to Users' Domain Expertise via Query-Wise Semantic and Keystroke Behavioral Cues
ExPerT infers query-specific user expertise from semantic text and keystroke dynamics via LLM prompting to adapt response generation, cutting inference error 65.7% and raising satisfaction 17.52% in a 40-participant study.
-
Discovering Implicit Large Language Model Alignment Objectives
Obj-Disco decomposes LLM alignment reward signals into sparse weighted combinations of interpretable natural language objectives via iterative analysis of behavioral changes across checkpoints, capturing over 90% of observed reward behavior.