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Found 15 papers, 7 papers with code
Learning Syntax Without Planting Trees: Understanding When and Why Transformers Generalize Hierarchically
Transformers trained on natural language data have been shown to learn its hierarchical structure and generalize to sentences with unseen syntactic structures without explicitly encoding any structural bias.
DIALECTBENCH: A NLP Benchmark for Dialects, Varieties, and Closely-Related Languages
This allows for a comprehensive evaluation of NLP system performance on different language varieties.
MAFIA: Multi-Adapter Fused Inclusive LanguAge Models
In this work, we aim to modularly debias a pretrained language model across multiple dimensions.
On Evaluating and Mitigating Gender Biases in Multilingual Settings
While understanding and removing gender biases in language models has been a long-standing problem in Natural Language Processing, prior research work has primarily been limited to English.
In-Context Learning through the Bayesian Prism
One of the main discoveries in this line of research has been that for several function classes, such as linear regression, transformers successfully generalize to new functions in the class.
Breaking Language Barriers with a LEAP: Learning Strategies for Polyglot LLMs
Our results show substantial advancements in multilingual understanding and generation across a diverse range of languages.
MEGA: Multilingual Evaluation of Generative AI
Most studies on generative LLMs have been restricted to English and it is unclear how capable these models are at understanding and generating text in other languages.
On the Calibration of Massively Multilingual Language Models
Massively Multilingual Language Models (MMLMs) have recently gained popularity due to their surprising effectiveness in cross-lingual transfer.
Multi Task Learning For Zero Shot Performance Prediction of Multilingual Models
Massively Multilingual Transformer based Language Models have been observed to be surprisingly effective on zero-shot transfer across languages, though the performance varies from language to language depending on the pivot language(s) used for fine-tuning.
Beyond Static Models and Test Sets: Benchmarking the Potential of Pre-trained Models Across Tasks and Languages
Although recent Massively Multilingual Language Models (MMLMs) like mBERT and XLMR support around 100 languages, most existing multilingual NLP benchmarks provide evaluation data in only a handful of these languages with little linguistic diversity.
On the Economics of Multilingual Few-shot Learning: Modeling the Cost-Performance Trade-offs of Machine Translated and Manual Data
Borrowing ideas from {\em Production functions} in micro-economics, in this paper we introduce a framework to systematically evaluate the performance and cost trade-offs between machine-translated and manually-created labelled data for task-specific fine-tuning of massively multilingual language models.
Global Readiness of Language Technology for Healthcare: What would it Take to Combat the Next Pandemic?
The COVID-19 pandemic has brought out both the best and worst of language technology (LT).
On the Practical Ability of Recurrent Neural Networks to Recognize Hierarchical Languages
We find that while recurrent models generalize nearly perfectly if the lengths of the training and test strings are from the same range, they perform poorly if the test strings are longer.
On the Ability and Limitations of Transformers to Recognize Formal Languages
Our analysis also provides insights on the role of self-attention mechanism in modeling certain behaviors and the influence of positional encoding schemes on the learning and generalization abilities of the model.
Syntax-guided Controlled Generation of Paraphrases
In these methods, syntactic-guidance is sourced from a separate exemplar sentence.
