Search Results for author:
Found 17 papers, 8 papers with code
CIVICS: Building a Dataset for Examining Culturally-Informed Values in Large Language Models
This paper introduces the "CIVICS: Culturally-Informed & Values-Inclusive Corpus for Societal impacts" dataset, designed to evaluate the social and cultural variation of Large Language Models (LLMs) across multiple languages and value-sensitive topics.
Power Hungry Processing: Watts Driving the Cost of AI Deployment?
Recent years have seen a surge in the popularity of commercial AI products based on generative, multi-purpose AI systems promising a unified approach to building machine learning (ML) models into technology.
Position: Key Claims in LLM Research Have a Long Tail of Footnotes
Much of the recent discourse within the ML community has been centered around Large Language Models (LLMs), their functionality and potential -- yet not only do we not have a working definition of LLMs, but much of this discourse relies on claims and assumptions that are worth re-examining.
The ROOTS Search Tool: Data Transparency for LLMs
ROOTS is a 1. 6TB multilingual text corpus developed for the training of BLOOM, currently the largest language model explicitly accompanied by commensurate data governance efforts.
Counting Carbon: A Survey of Factors Influencing the Emissions of Machine Learning
Machine learning (ML) requires using energy to carry out computations during the model training process.
Measuring Data
We identify the task of measuring data to quantitatively characterize the composition of machine learning data and datasets.
BLOOM: A 176B-Parameter Open-Access Multilingual Language Model
Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions.
Estimating the Carbon Footprint of BLOOM, a 176B Parameter Language Model
Progress in machine learning (ML) comes with a cost to the environment, given that training ML models requires significant computational resources, energy and materials.
Evaluate & Evaluation on the Hub: Better Best Practices for Data and Model Measurements
We introduce Evaluate and Evaluation on the Hub --a set of tools to facilitate the evaluation of models and datasets in ML.
Bugs in the Data: How ImageNet Misrepresents Biodiversity
We find that many of the classes are ill-defined or overlapping, and that 12% of the images are incorrectly labeled, with some classes having >90% of images incorrect.
Measuring the Carbon Intensity of AI in Cloud Instances
By providing unprecedented access to computational resources, cloud computing has enabled rapid growth in technologies such as machine learning, the computational demands of which incur a high energy cost and a commensurate carbon footprint.
Data Governance in the Age of Large-Scale Data-Driven Language Technology
The recent emergence and adoption of Machine Learning technology, and specifically of Large Language Models, has drawn attention to the need for systematic and transparent management of language data.
Metaethical Perspectives on 'Benchmarking' AI Ethics
In this paper, drawing upon research in moral philosophy and metaethics, we argue that it is impossible to develop such a benchmark.
A Framework for Deprecating Datasets: Standardizing Documentation, Identification, and Communication
Datasets are central to training machine learning (ML) models.
ClimateGAN: Raising Climate Change Awareness by Generating Images of Floods
Climate change is a major threat to humanity, and the actions required to prevent its catastrophic consequences include changes in both policy-making and individual behaviour.
Ensuring the Inclusive Use of Natural Language Processing in the Global Response to COVID-19
Natural language processing (NLP) plays a significant role in tools for the COVID-19 pandemic response, from detecting misinformation on social media to helping to provide accurate clinical information or summarizing scientific research.
What's in the Box? A Preliminary Analysis of Undesirable Content in the Common Crawl Corpus
Whereas much of the success of the current generation of neural language models has been driven by increasingly large training corpora, relatively little research has been dedicated to analyzing these massive sources of textual data.
