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A team project working towards AI safety applied to Google's Gemma-4-E2B model
Jailbreaking Gemma 4 E2B models to expose safety gaps in AI lesson planning. Our goal is to highlight developer accountability and urge educators to critically review AI outputs.
We tried to trick Gemma into cheating - roleplay, sob stories, fake prof permission. Tested 2 languages, 4 judges, and if it acts different when "watched." Results are spicy
Our plant toxicology and poison control assistant is a model people turn to when they suspect a poisonous plant exposure and need fast, accurate guidance.
Evaluate Gemma’s robustness against adversarial evasion by measuring how readily it can be induced to misclassify phishing emails as legitimate through iterative adversarial rewriting.
A multilingual content moderation scorecard comparing Gemma-base and Gemma-IT on harm recognition, refusal calibration, and safe steering across red-teaming prompts.
Investigating the safety of an LLM autograder performance on student essays.
Project focusing on the personal finance domain regarding budgeting, debt management, credit, investments, and more.
Our project topic is a Love Island based contextual self improvement generator
InstiGuard-Gemma: A 5-metric enterprise safety scorecard auditing Gemma-4-E2B variants with a 3-model judge panel, cross-lingual testing, and a novel long-context constraint decay metric.
Evaluating the Gemma LLM on it's tutor abilities for elementary science.
UVA - DS6051 Decoding LLMs Hackathon
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