MASSIVE: A 1M-example multilingual natural language understanding dataset with 51 typologically-diverse languages

J FitzGerald, C Hench, C Peris, S Mackie… - arxiv preprint arxiv …, 2022‏ - arxiv.org
We present the MASSIVE dataset--Multilingual Amazon Slu resource package (SLURP) for
Slot-filling, Intent classification, and Virtual assistant Evaluation. MASSIVE contains 1M …

Privacy in the time of language models

C Peris, C Dupuy, J Majmudar, R Parikh… - Proceedings of the …, 2023‏ - dl.acm.org
Pretrained large language models (LLMs) have consistently shown state-of-the-art
performance across multiple natural language processing (NLP) tasks. These models are of …

A survey on knowledge editing of neural networks

V Mazzia, A Pedrani, A Caciolai… - … on Neural Networks …, 2024‏ - ieeexplore.ieee.org
Deep neural networks are becoming increasingly pervasive in academia and industry,
matching and surpassing human performance in a wide variety of fields and related tasks …

A comprehensive review of large language models: issues and solutions in learning environments

T Shahzad, T Mazhar, MU Tariq, W Ahmad… - Discover …, 2025‏ - Springer
A significant advancement in artificial intelligence is the development of large language
models (LLMs). Despite opposition and explicit bans by some authorities, LLMs continue to …

LINGUIST: Language model instruction tuning to generate annotated utterances for intent classification and slot tagging

A Rosenbaum, S Soltan, W Hamza, Y Versley… - arxiv preprint arxiv …, 2022‏ - arxiv.org
We present LINGUIST, a method for generating annotated data for Intent Classification and
Slot Tagging (IC+ ST), via fine-tuning AlexaTM 5B, a 5-billion-parameter multilingual …

A mixed-methods approach to understanding user trust after voice assistant failures

A Baughan, X Wang, A Liu, A Mercurio… - Proceedings of the 2023 …, 2023‏ - dl.acm.org
Despite huge gains in performance in natural language understanding via large language
models in recent years, voice assistants still often fail to meet user expectations. In this study …

Optimal transport posterior alignment for cross-lingual semantic parsing

T Sherborne, T Hosking, M Lapata - Transactions of the Association …, 2023‏ - direct.mit.edu
Cross-lingual semantic parsing transfers parsing capability from a high-resource language
(eg, English) to low-resource languages with scarce training data. Previous work has …

Honey, I shrunk the language: Language model behavior at reduced scale

V Deshpande, D Pechi, S Thatte, V Lialin… - arxiv preprint arxiv …, 2023‏ - arxiv.org
In recent years, language models have drastically grown in size, and the abilities of these
models have been shown to improve with scale. The majority of recent scaling laws studies …

Efficient fine-tuning large language models for knowledge-aware response planning

M Nguyen, KC Kishan, T Nguyen, A Chadha… - … European Conference on …, 2023‏ - Springer
Abstract Large Language Models (LLMs) have shown impressive emergent language
capabilities, especially in applications with high ambiguity, such as language reasoning and …

[HTML][HTML] A Survey on Knowledge Distillation: Recent Advancements

A Moslemi, A Briskina, Z Dang, J Li - Machine Learning with Applications, 2024‏ - Elsevier
Deep learning has achieved notable success across academia, medicine, and industry. Its
ability to identify complex patterns in large-scale data and to manage millions of parameters …