161 citations · 373 across the 13 of their papers we have counts for
20 papers
Position: Key Claims in LLM Research Have a Long Tail of Footnotes
Anna Rogers, Alexandra Sasha Luccioni
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 worki…
Using Sequences of Life-events to Predict Human Lives
Germans Savcisens, Tina Eliassi-Rad, Lars Kai Hansen +5
Over the past decade, machine learning has revolutionized computers' ability to analyze text through flexible computational models. Due to their structural similarity to written la…
The BigScience ROOTS Corpus: A 1.6TB Composite Multilingual Dataset
Hugo Laurençon, Lucile Saulnier, Thomas Wang +51
As language models grow ever larger, the need for large-scale high-quality text datasets has never been more pressing, especially in multilingual settings. The BigScience workshop,…
The ROOTS Search Tool: Data Transparency for LLMs
Aleksandra Piktus, Christopher Akiki, Paulo Villegas +5
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.…
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…
Machine Reading, Fast and Slow: When Do Models "Understand" Language?
Sagnik Ray Choudhury, Anna Rogers, Isabelle Augenstein
Two of the most fundamental challenges in Natural Language Understanding (NLU) at present are: (a) how to establish whether deep learning-based models score highly on NLU benchmark…
What Factors Should Paper-Reviewer Assignments Rely On? Community Perspectives on Issues and Ideals in Conference Peer-Review
Terne Sasha Thorn Jakobsen, Anna Rogers
Both scientific progress and individual researcher careers depend on the quality of peer review, which in turn depends on paper-reviewer matching. Surprisingly, this problem has be…
Outliers Dimensions that Disrupt Transformers Are Driven by Frequency
Giovanni Puccetti, Anna Rogers, Aleksandr Drozd +1
While Transformer-based language models are generally very robust to pruning, there is the recently discovered outlier phenomenon: disabling only 48 out of 110M parameters in BERT-…
Generalization in NLI: Ways (Not) To Go Beyond Simple Heuristics
Prajjwal Bhargava, Aleksandr Drozd, Anna Rogers
Much of recent progress in NLU was shown to be due to models' learning dataset-specific heuristics. We conduct a case study of generalization in NLI (from MNLI to the adversarially…
Just What do You Think You're Doing, Dave?' A Checklist for Responsible Data Use in NLP
Anna Rogers, Tim Baldwin, Kobi Leins
A key part of the NLP ethics movement is responsible use of data, but exactly what that means or how it can be best achieved remain unclear. This position paper discusses the core…
QA Dataset Explosion: A Taxonomy of NLP Resources for Question Answering and Reading Comprehension
Anna Rogers, Matt Gardner, Isabelle Augenstein
Alongside huge volumes of research on deep learning models in NLP in the recent years, there has been also much work on benchmark datasets needed to track modeling progress. Questi…
On the Interaction of Belief Bias and Explanations
Ana Valeria Gonzalez, Anna Rogers, Anders Søgaard
A myriad of explainability methods have been proposed in recent years, but there is little consensus on how to evaluate them. While automatic metrics allow for quick benchmarking,…
BERT Busters: Outlier Dimensions that Disrupt Transformers
Olga Kovaleva, Saurabh Kulshreshtha, Anna Rogers +1
Multiple studies have shown that Transformers are remarkably robust to pruning. Contrary to this received wisdom, we demonstrate that pre-trained Transformer encoders are surprisin…
Changing the World by Changing the Data
Anna Rogers
NLP community is currently investing a lot more research and resources into development of deep learning models than training data. While we have made a lot of progress, it is now…
What Can We Do to Improve Peer Review in NLP?
Anna Rogers, Isabelle Augenstein
Peer review is our best tool for judging the quality of conference submissions, but it is becoming increasingly spurious. We argue that a part of the problem is that the reviewers…
When BERT Plays the Lottery, All Tickets Are Winning
Sai Prasanna, Anna Rogers, Anna Rumshisky
Large Transformer-based models were shown to be reducible to a smaller number of self-attention heads and layers. We consider this phenomenon from the perspective of the lottery ti…
A Primer in BERTology: What we know about how BERT works
Anna Rogers, Olga Kovaleva, Anna Rumshisky
Transformer-based models have pushed state of the art in many areas of NLP, but our understanding of what is behind their success is still limited. This paper is the first survey o…
Revealing the Dark Secrets of BERT
Olga Kovaleva, Alexey Romanov, Anna Rogers +1
BERT-based architectures currently give state-of-the-art performance on many NLP tasks, but little is known about the exact mechanisms that contribute to its success. In the curren…
NarrativeTime: Dense Temporal Annotation on a Timeline
Anna Rogers, Marzena Karpinska, Ankita Gupta +3
For the past decade, temporal annotation has been sparse: only a small portion of event pairs in a text was annotated. We present NarrativeTime, the first timeline-based annotation…
Adversarial Decomposition of Text Representation
Alexey Romanov, Anna Rumshisky, Anna Rogers +1
In this paper, we present a method for adversarial decomposition of text representation. This method can be used to decompose a representation of an input sentence into several ind…