65 citations · 192 across the 33 of their papers we have counts for
26 papers · 1 filter
Aligning Stuttered-Speech Research with End-User Needs: Scoping Review, Survey, and Guidelines
Hawau Olamide Toyin, Mutiah Apampa, Toluwani Aremu +6
Atypical speech is receiving greater attention in speech technology research, but much of this work unfolds with limited interdisciplinary dialogue. For stuttered speech in particu…
Online Learning Defense against Iterative Jailbreak Attacks via Prompt Optimization
Masahiro Kaneko, Zeerak Talat, Timothy Baldwin
Iterative jailbreak methods that repeatedly rewrite and input prompts into large language models (LLMs) to induce harmful outputs -- using the model's previous responses to guide e…
Personal Attribute Leakage in Federated Speech Models
Hamdan Al-Ali, Ali Reza Ghavamipour, Tommaso Caselli +3
Federated learning is a common method for privacy-preserving training of machine learning models. In this paper, we analyze the vulnerability of ASR models to attribute inference a…
Code-Switching in End-to-End Automatic Speech Recognition: A Systematic Literature Review
Maha Tufail Agro, Atharva Kulkarni, Karima Kadaoui +2
Motivated by a growing research interest into automatic speech recognition (ASR), and the growing body of work for languages in which code-switching (CS) often occurs, we present a…
IYKYK: Using language models to decode extremist cryptolects
Christine de Kock, Arij Riabi, Zeerak Talat +3
Extremist groups develop complex in-group language, also referred to as cryptolects, to exclude or mislead outsiders. We investigate the ability of current language technologies to…
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…
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…
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…
Mirages: On Anthropomorphism in Dialogue Systems
Gavin Abercrombie, Amanda Cercas Curry, Tanvi Dinkar +2
Automated dialogue or conversational systems are anthropomorphised by developers and personified by users. While a degree of anthropomorphism may be inevitable due to the choice of…
Thorny Roses: Investigating the Dual Use Dilemma in Natural Language Processing
Lucie-Aimée Kaffee, Arnav Arora, Zeerak Talat +1
Dual use, the intentional, harmful reuse of technology and scientific artefacts, is a problem yet to be well-defined within the context of Natural Language Processing (NLP). Howeve…
BLOOM: A 176B-Parameter Open-Access Multilingual Language Model
BigScience Workshop, :, Teven Le Scao +391
Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to wi…
Back to the Future: On Potential Histories in NLP
Zeerak Talat, Anne Lauscher
Machine learning and NLP require the construction of datasets to train and fine-tune models. In this context, previous work has demonstrated the sensitivity of these data sets. For…
Multilingual HateCheck: Functional Tests for Multilingual Hate Speech Detection Models
Paul Röttger, Haitham Seelawi, Debora Nozza +2
Hate speech detection models are typically evaluated on held-out test sets. However, this risks painting an incomplete and potentially misleading picture of model performance becau…
Documenting Geographically and Contextually Diverse Data Sources: The BigScience Catalogue of Language Data and Resources
Angelina McMillan-Major, Zaid Alyafeai, Stella Biderman +15
In recent years, large-scale data collection efforts have prioritized the amount of data collected in order to improve the modeling capabilities of large language models. This prio…
A Word on Machine Ethics: A Response to Jiang et al. (2021)
Zeerak Talat, Hagen Blix, Josef Valvoda +3
Ethics is one of the longest standing intellectual endeavors of humanity. In recent years, the fields of AI and NLP have attempted to wrangle with how learning systems that interac…
A Survey of Race, Racism, and Anti-Racism in NLP
Anjalie Field, Su Lin Blodgett, Zeerak Waseem +1
Despite inextricable ties between race and language, little work has considered race in NLP research and development. In this work, we survey 79 papers from the ACL anthology that…
Dynabench: Rethinking Benchmarking in NLP
Douwe Kiela, Max Bartolo, Yixin Nie +16
We introduce Dynabench, an open-source platform for dynamic dataset creation and model benchmarking. Dynabench runs in a web browser and supports human-and-model-in-the-loop datase…
Learning from the Worst: Dynamically Generated Datasets to Improve Online Hate Detection
Bertie Vidgen, Tristan Thrush, Zeerak Waseem +1
We present a human-and-model-in-the-loop process for dynamically generating datasets and training better performing and more robust hate detection models. We provide a new dataset…
HateCheck: Functional Tests for Hate Speech Detection Models
Paul Röttger, Bertram Vidgen, Dong Nguyen +3
Detecting online hate is a difficult task that even state-of-the-art models struggle with. Typically, hate speech detection models are evaluated by measuring their performance on h…