65 citations · 192 across the 33 of their papers we have counts for
11 papers · 1 filter
Exploitation All the Way Down: Calling out the Root Cause of Bad Online Experiences for Users of the "Majority World"
Hellina Hailu Nigatu, Zeerak Talat
Global Majority users are exposed to multitudes of harm when interacting with online platforms. This essay illuminates how exploitation in the advances of Artificial Intelligence i…
The Only Way is Ethics: A Guide to Ethical Research with Large Language Models
Eddie L. Ungless, Nikolas Vitsakis, Zeerak Talat +5
There is a significant body of work looking at the ethical considerations of large language models (LLMs): critiquing tools to measure performance and harms; proposing toolkits to…
A Capabilities Approach to Studying Bias and Harm in Language Technologies
Hellina Hailu Nigatu, Zeerak Talat
Mainstream Natural Language Processing (NLP) research has ignored the majority of the world's languages. In moving from excluding the majority of the world's languages to blindly a…
Ethics Whitepaper: Whitepaper on Ethical Research into Large Language Models
Eddie L. Ungless, Nikolas Vitsakis, Zeerak Talat +5
This whitepaper offers an overview of the ethical considerations surrounding research into or with large language models (LLMs). As LLMs become more integrated into widely used app…
Understanding "Democratization" in NLP and ML Research
Arjun Subramonian, Vagrant Gautam, Dietrich Klakow +1
Recent improvements in natural language processing (NLP) and machine learning (ML) and increased mainstream adoption have led to researchers frequently discussing the "democratizat…
Exploring the Limitations of Detecting Machine-Generated Text
Jad Doughman, Osama Mohammed Afzal, Hawau Olamide Toyin +3
Recent improvements in the quality of the generations by large language models have spurred research into identifying machine-generated text. Such work often presents high-performi…
The Perspectivist Paradigm Shift: Assumptions and Challenges of Capturing Human Labels
Eve Fleisig, Su Lin Blodgett, Dan Klein +1
Longstanding data labeling practices in machine learning involve collecting and aggregating labels from multiple annotators. But what should we do when annotators disagree? Though…
Classist Tools: Social Class Correlates with Performance in NLP
Amanda Cercas Curry, Giuseppe Attanasio, Zeerak Talat +1
Since the foundational work of William Labov on the social stratification of language (Labov, 1964), linguistics has made concentrated efforts to explore the links between sociodem…
Impoverished Language Technology: The Lack of (Social) Class in NLP
Amanda Cercas Curry, Zeerak Talat, Dirk Hovy
Since Labov's (1964) foundational work on the social stratification of language, linguistics has dedicated concerted efforts towards understanding the relationships between socio-d…
Subjective ? On the Danger of Conflating Hate and Offence in Abusive Language Detection
Amanda Cercas Curry, Gavin Abercrombie, Zeerak Talat
Natural language processing research has begun to embrace the notion of annotator subjectivity, motivated by variations in labelling. This approach understands each annotator's vie…
Zero-shot Sentiment Analysis in Low-Resource Languages Using a Multilingual Sentiment Lexicon
Fajri Koto, Tilman Beck, Zeerak Talat +2
Improving multilingual language models capabilities in low-resource languages is generally difficult due to the scarcity of large-scale data in those languages. In this paper, we r…