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Found 7 papers, 3 papers with code
Learning to Prioritize: Precision-Driven Sentence Filtering for Long Text Summarization
Neural text summarization has shown great potential in recent years.
ASSERT: Automated Safety Scenario Red Teaming for Evaluating the Robustness of Large Language Models
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.
Let's Think Frame by Frame with VIP: A Video Infilling and Prediction Dataset for Evaluating Video Chain-of-Thought
Despite exciting recent results showing vision-language systems' capacity to reason about images using natural language, their capacity for video reasoning remains under-explored.
Visual Chain of Thought: Bridging Logical Gaps with Multimodal Infillings
Recent advances in large language models elicit reasoning in a chain-of-thought that allows models to decompose problems in a human-like fashion.
Users are the North Star for AI Transparency
We conduct a broad literature survey, identifying many clusters of similar conceptions of transparency, tying each back to our north star with analysis of how it furthers or hinders our ideal AI transparency goals.
Foveate, Attribute, and Rationalize: Towards Physically Safe and Trustworthy AI
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.
Mitigating Covertly Unsafe Text within Natural Language Systems
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.
