VOW formulates LLM watermark detection as a secure two-party computation using a Verifiable Oblivious Pseudorandom Function to achieve private and cryptographically verifiable detection.
Exploring the deceptive power of llm-generated fake news: A study of real-world detection challenges
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
verdicts
UNVERDICTED 3roles
background 1polarities
background 1representative citing papers
LiveFact is a new time-aware benchmark that evaluates LLMs on reasoning with dynamic and incomplete information for fake news detection, identifying a significant reasoning gap in model behavior.
An all-pay auction for AI model approval derives Nash equilibria in which rational firms exceed compliance thresholds and reports 20% higher compliance and 15% higher participation than baseline rules.
citing papers explorer
-
VOW: Verifiable and Oblivious Watermark Detection for Large Language Models
VOW formulates LLM watermark detection as a secure two-party computation using a Verifiable Oblivious Pseudorandom Function to achieve private and cryptographically verifiable detection.
-
LiveFact: A Dynamic, Time-Aware Benchmark for LLM-Driven Fake News Detection
LiveFact is a new time-aware benchmark that evaluates LLMs on reasoning with dynamic and incomplete information for fake news detection, identifying a significant reasoning gap in model behavior.
-
Auction-Based Regulation for Artificial Intelligence
An all-pay auction for AI model approval derives Nash equilibria in which rational firms exceed compliance thresholds and reports 20% higher compliance and 15% higher participation than baseline rules.