73 research outputs found
Patterns and Variation in English Language Discourse
The publication is reviewed post-conference proceedings from the international 9th Brno Conference on Linguistics Studies in English, held on 16–17 September 2021 and organised by the Faculty of Education, Masaryk University in Brno. The papers revolve around the themes of patterns and variation in specialised discourses (namely the media, academic, business, tourism, educational and learner discourses), effective interaction between the addressor and addressees and the current trends and development in specialised discourses. The principal methodological perspectives are the comparative approach involving discourses in English and another language, critical and corpus analysis, as well as identification of pragmatic strategies and appropriate rhetorical means. The authors of papers are researchers from the Czech Republic, Italy, Luxembourg, Serbia and Georgia
A Survey of GPT-3 Family Large Language Models Including ChatGPT and GPT-4
Large language models (LLMs) are a special class of pretrained language
models obtained by scaling model size, pretraining corpus and computation.
LLMs, because of their large size and pretraining on large volumes of text
data, exhibit special abilities which allow them to achieve remarkable
performances without any task-specific training in many of the natural language
processing tasks. The era of LLMs started with OpenAI GPT-3 model, and the
popularity of LLMs is increasing exponentially after the introduction of models
like ChatGPT and GPT4. We refer to GPT-3 and its successor OpenAI models,
including ChatGPT and GPT4, as GPT-3 family large language models (GLLMs). With
the ever-rising popularity of GLLMs, especially in the research community,
there is a strong need for a comprehensive survey which summarizes the recent
research progress in multiple dimensions and can guide the research community
with insightful future research directions. We start the survey paper with
foundation concepts like transformers, transfer learning, self-supervised
learning, pretrained language models and large language models. We then present
a brief overview of GLLMs and discuss the performances of GLLMs in various
downstream tasks, specific domains and multiple languages. We also discuss the
data labelling and data augmentation abilities of GLLMs, the robustness of
GLLMs, the effectiveness of GLLMs as evaluators, and finally, conclude with
multiple insightful future research directions. To summarize, this
comprehensive survey paper will serve as a good resource for both academic and
industry people to stay updated with the latest research related to GPT-3
family large language models.Comment: Preprint under review, 58 page
Language variation, automatic speech recognition and algorithmic bias
In this thesis, I situate the impacts of automatic speech recognition systems in relation to sociolinguistic theory (in particular drawing on concepts of language variation, language ideology
and language policy) and contemporary debates in AI ethics (especially regarding algorithmic
bias and fairness). In recent years, automatic speech recognition systems, alongside other
language technologies, have been adopted by a growing number of users and have been embedded in an increasing number of algorithmic systems. This expansion into new application
domains and language varieties can be understood as an expansion into new sociolinguistic
contexts. In this thesis, I am interested in how automatic speech recognition tools interact
with this sociolinguistic context, and how they affect speakers, speech communities and their
language varieties.
Focussing on commercial automatic speech recognition systems for British Englishes, I first
explore the extent and consequences of performance differences of these systems for different user groups depending on their linguistic background. When situating this predictive bias
within the wider sociolinguistic context, it becomes apparent that these systems reproduce and
potentially entrench existing linguistic discrimination and could therefore cause direct and indirect harms to already marginalised speaker groups. To understand the benefits and potentials
of automatic transcription tools, I highlight two case studies: transcribing sociolinguistic data
in English and transcribing personal voice messages in isiXhosa. The central role of the sociolinguistic context in developing these tools is emphasised in this comparison. Design choices,
such as the choice of training data, are particularly consequential because they interact with existing processes of language standardisation. To understand the impacts of these choices, and
the role of the developers making them better, I draw on theory from language policy research
and critical data studies. These conceptual frameworks are intended to help practitioners and
researchers in anticipating and mitigating predictive bias and other potential harms of speech
technologies. Beyond looking at individual choices, I also investigate the discourses about language variation and linguistic diversity deployed in the context of language technologies. These
discourses put forward by researchers, developers and commercial providers not only have a
direct effect on the wider sociolinguistic context, but they also highlight how this context (e.g.,
existing beliefs about language(s)) affects technology development. Finally, I explore ways of
building better automatic speech recognition tools, focussing in particular on well-documented,
naturalistic and diverse benchmark datasets. However, inclusive datasets are not necessarily
a panacea, as they still raise important questions about the nature of linguistic data and language variation (especially in relation to identity), and may not mitigate or prevent all potential
harms of automatic speech recognition systems as embedded in larger algorithmic systems
and sociolinguistic contexts
Donkii: Can Annotation Error Detection Methods Find Errors in Instruction-Tuning Datasets?
Instruction-tuning has become an integral part of training pipelines for
Large Language Models (LLMs) and has been shown to yield strong performance
gains. In an orthogonal line of research, Annotation Error Detection (AED) has
emerged as a tool for detecting quality issues of gold-standard labels. But so
far, the application of AED methods is limited to discriminative settings. It
is an open question how well AED methods generalize to generative settings
which are becoming widespread via generative LLMs. In this work, we present a
first and new benchmark for AED on instruction-tuning data: Donkii. It
encompasses three instruction-tuning datasets enriched with annotations by
experts and semi-automatic methods. We find that all three datasets contain
clear-cut errors that sometimes directly propagate into instruction-tuned LLMs.
We propose four AED baselines for the generative setting and evaluate them
comprehensively on the newly introduced dataset. Our results demonstrate that
choosing the right AED method and model size is indeed crucial, thereby
deriving practical recommendations. To gain insights, we provide a first
case-study to examine how the quality of the instruction-tuning datasets
influences downstream performance
Geographic information extraction from texts
A large volume of unstructured texts, containing valuable geographic information, is available online. This information – provided implicitly or explicitly – is useful not only for scientific studies (e.g., spatial humanities) but also for many practical applications (e.g., geographic information retrieval). Although large progress has been achieved in geographic information extraction from texts, there are still unsolved challenges and issues, ranging from methods, systems, and data, to applications and privacy. Therefore, this workshop will provide a timely opportunity to discuss the recent advances, new ideas, and concepts but also identify research gaps in geographic information extraction
LINGO : Visually Debiasing Natural Language Instructions to Support Task Diversity
Cross-task generalization is a significant outcome that defines mastery in
natural language understanding. Humans show a remarkable aptitude for this, and
can solve many different types of tasks, given definitions in the form of
textual instructions and a small set of examples. Recent work with pre-trained
language models mimics this learning style: users can define and exemplify a
task for the model to attempt as a series of natural language prompts or
instructions. While prompting approaches have led to higher cross-task
generalization compared to traditional supervised learning, analyzing 'bias' in
the task instructions given to the model is a difficult problem, and has thus
been relatively unexplored. For instance, are we truly modeling a task, or are
we modeling a user's instructions? To help investigate this, we develop LINGO,
a novel visual analytics interface that supports an effective, task-driven
workflow to (1) help identify bias in natural language task instructions, (2)
alter (or create) task instructions to reduce bias, and (3) evaluate
pre-trained model performance on debiased task instructions. To robustly
evaluate LINGO, we conduct a user study with both novice and expert instruction
creators, over a dataset of 1,616 linguistic tasks and their natural language
instructions, spanning 55 different languages. For both user groups, LINGO
promotes the creation of more difficult tasks for pre-trained models, that
contain higher linguistic diversity and lower instruction bias. We additionally
discuss how the insights learned in developing and evaluating LINGO can aid in
the design of future dashboards that aim to minimize the effort involved in
prompt creation across multiple domains.Comment: 13 pages, 6 figures, Eurovis 202
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