Search Results for author:
Found 9 papers, 6 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.
Speech Wikimedia: A 77 Language Multilingual Speech Dataset
The Speech Wikimedia Dataset is a publicly available compilation of audio with transcriptions extracted from Wikimedia Commons.
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.
LSH methods for data deduplication in a Wikipedia artificial dataset
This paper illustrates locality sensitive hasing (LSH) models for the identification and removal of nearly redundant data in a text dataset.
The People's Speech: A Large-Scale Diverse English Speech Recognition Dataset for Commercial Usage
The People's Speech is a free-to-download 30, 000-hour and growing supervised conversational English speech recognition dataset licensed for academic and commercial usage under CC-BY-SA (with a CC-BY subset).
