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Found 30 papers, 7 papers with code
‘Just What do You Think You’re Doing, Dave?’ A Checklist for Responsible Data Use in NLP
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.
Machine Reading, Fast and Slow: When Do Models “Understand” Language?
Two of the most fundamental issues in Natural Language Understanding (NLU) at present are: (a) how it can established whether deep learning-based models score highly on NLU benchmarks for the ”right” reasons; and (b) what those reasons would even be.
Mind your Language (Model): Fact-Checking LLMs and their Role in NLP Research and Practice
This position paper contributes a definition of LLMs, explicates some of the assumptions made regarding their functionality, and outlines the existing evidence for and against them.
Using Sequences of Life-events to Predict Human Lives
We can also represent human lives in a way that shares this structural similarity to language.
The BigScience ROOTS Corpus: A 1.6TB Composite Multilingual Dataset
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 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.
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.
Machine Reading, Fast and Slow: When Do Models "Understand" Language?
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 benchmarks for the 'right' reasons; and (b) to understand what those reasons would even be.
Outliers Dimensions that Disrupt Transformers Are Driven by Frequency
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-base drops its performance by nearly 30% on MNLI.
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.
What Factors Should Paper-Reviewer Assignments Rely On? Community Perspectives on Issues and Ideals in Conference Peer-Review
Both scientific progress and individual researcher careers depend on the quality of peer review, which in turn depends on paper-reviewer matching.
Generalization in NLI: Ways (Not) To Go Beyond Simple Heuristics
Much of recent progress in NLU was shown to be due to models' learning dataset-specific heuristics.
Just What do You Think You're Doing, Dave?' A Checklist for Responsible Data Use in NLP
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.
QA Dataset Explosion: A Taxonomy of NLP Resources for Question Answering and Reading Comprehension
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.
On the Interaction of Belief Bias and Explanations
A myriad of explainability methods have been proposed in recent years, but there is little consensus on how to evaluate them.
Changing the World by Changing the Data
NLP community is currently investing a lot more research and resources into development of deep learning models than training data.
BERT Busters: Outlier Dimensions that Disrupt Transformers
Multiple studies have shown that Transformers are remarkably robust to pruning.
A guide to the dataset explosion in QA, NLI, and commonsense reasoning
Question answering, natural language inference and commonsense reasoning are increasingly popular as general NLP system benchmarks, driving both modeling and dataset work.
What Can We Do to Improve Peer Review in NLP?
Peer review is our best tool for judging the quality of conference submissions, but it is becoming increasingly spurious.
When BERT Plays the Lottery, All Tickets Are Winning
Large Transformer-based models were shown to be reducible to a smaller number of self-attention heads and layers.
A Primer in BERTology: What we know about how BERT works
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.
Calls to Action on Social Media: Detection, Social Impact, and Censorship Potential
Calls to action on social media are known to be effective means of mobilization in social movements, and a frequent target of censorship.
NarrativeTime: Dense Temporal Annotation on a Timeline
For the past decade, temporal annotation has been sparse: only a small portion of event pairs in a text was annotated.
Revealing the Dark Secrets of BERT
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.
Adversarial Decomposition of Text Representation
We show that the proposed method is capable of fine-grained controlled change of these aspects of the input sentence.
What's in Your Embedding, And How It Predicts Task Performance
Attempts to find a single technique for general-purpose intrinsic evaluation of word embeddings have so far not been successful.
RuSentiment: An Enriched Sentiment Analysis Dataset for Social Media in Russian
This paper presents RuSentiment, a new dataset for sentiment analysis of social media posts in Russian, and a new set of comprehensive annotation guidelines that are extensible to other languages.
Ranked #2 on
Sentiment Analysis
on RuSentiment
Subcharacter Information in Japanese Embeddings: When Is It Worth It?
Languages with logographic writing systems present a difficulty for traditional character-level models.
Investigating Different Syntactic Context Types and Context Representations for Learning Word Embeddings
The number of word embedding models is growing every year.
The (too Many) Problems of Analogical Reasoning with Word Vectors
This paper explores the possibilities of analogical reasoning with vector space models.
