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Alex Mei

4 accepted papers

2023

ASSERT: Automated Safety Scenario Red Teaming for Evaluating the Robustness of Large Language Models

EMNLP 2023long findings

As large language models are integrated into society, robustness toward a suite of prompts is increasingly important to maintain reliability in a high-variance environment.Robustness evaluations must comprehensively encapsulate the various settings in which a user may invoke an intelligent system. T…

Cited by 0SourcecodeScholar
2023

Foveate, Attribute, and Rationalize: Towards Physically Safe and Trustworthy AI

ACL 2023findings

Users’ physical safety is an increasing concern as the market for intelligent systems continues to grow, where unconstrained systems may recommend users dangerous actions that can lead to serious injury. Covertly unsafe text is an area of particular interest, as such text may arise from everyday sce…

2023

Let's Think Frame by Frame with VIP: A Video Infilling and Prediction Dataset for Evaluating Video Chain-of-Thought

EMNLP 2023long main

Despite exciting recent results showing vision-language systems’ capacity to reason about images using natural language, their capacity for video reasoning remains underexplored. We motivate framing video reasoning as the sequential understanding of a small number of keyframes, thereby leveraging th…

Cited by 0SourcecodeScholar
2022

Mitigating Covertly Unsafe Text within Natural Language Systems

EMNLP 2022finding

An increasingly prevalent problem for intelligent technologies is text safety, as uncontrolled systems may generate recommendations to their users that lead to injury or life-threatening consequences. However, the degree of explicitness of a generated statement that can cause physical harm varies. I…

Cited by 7SourcePDFScholar