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LLM Safety: Temporal and Linguistic Vulnerabilities

Published:Dec 31, 2025 01:40
1 min read
ArXiv

Analysis

This paper is significant because it challenges the assumption that LLM safety generalizes across languages and timeframes. It highlights a critical vulnerability in current LLMs, particularly for users in the Global South, by demonstrating how temporal framing and language can drastically alter safety performance. The study's focus on West African threat scenarios and the identification of 'Safety Pockets' underscores the need for more robust and context-aware safety mechanisms.
Reference

The study found a 'Temporal Asymmetry, where past-tense framing bypassed defenses (15.6% safe) while future-tense scenarios triggered hyper-conservative refusals (57.2% safe).'

Healthcare#Machine Learning📝 BlogAnalyzed: Dec 29, 2025 07:50

ML Innovation in Healthcare with Suchi Saria - #501

Published:Jul 15, 2021 20:32
1 min read
Practical AI

Analysis

This article summarizes a podcast episode featuring Suchi Saria, the founder and CEO of Bayesian Health, discussing the application of machine learning in healthcare. The conversation covers Saria's career path, the challenges of ML adoption in healthcare, and successful implementations. It highlights the slow integration of ML into the healthcare infrastructure and explores the state of healthcare data. The episode also focuses on Bayesian Health's goals and a study on real-time ML inference within an EMR setting. The article provides a concise overview of the key topics discussed in the podcast.
Reference

We discuss why it has taken so long for machine learning to become accepted and adopted by the healthcare infrastructure and where exactly we stand in the adoption process, where there have been “pockets” of tangible success.