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Found 6 papers, 5 papers with code
Clinical knowledge in LLMs does not translate to human interactions
Tested alone, LLMs complete the scenarios accurately, correctly identifying conditions in 94. 9% of cases and disposition in 56. 3% on average.
Findings of the BabyLM Challenge: Sample-Efficient Pretraining on Developmentally Plausible Corpora
These intensive resource demands limit the ability of researchers to train new models and use existing models as developmentally plausible cognitive models.
The PRISM Alignment Dataset: What Participatory, Representative and Individualised Human Feedback Reveals About the Subjective and Multicultural Alignment of Large Language Models
Human feedback is central to the alignment of Large Language Models (LLMs).
Adversarial Nibbler: An Open Red-Teaming Method for Identifying Diverse Harms in Text-to-Image Generation
By focusing on ``implicitly adversarial'' prompts (those that trigger T2I models to generate unsafe images for non-obvious reasons), we isolate a set of difficult safety issues that human creativity is well-suited to uncover.
Adversarial Nibbler: A Data-Centric Challenge for Improving the Safety of Text-to-Image Models
To address this need, we introduce the Adversarial Nibbler challenge.
DataPerf: Benchmarks for Data-Centric AI Development
Machine learning research has long focused on models rather than datasets, and prominent datasets are used for common ML tasks without regard to the breadth, difficulty, and faithfulness of the underlying problems.
