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Found 14 papers, 3 papers with code
Partial Federated Learning
Federated Learning (FL) is a popular algorithm to train machine learning models on user data constrained to edge devices (for example, mobile phones) due to privacy concerns.
On the steerability of large language models toward data-driven personas
Large language models (LLMs) are known to generate biased responses where the opinions of certain groups and populations are underrepresented.
Coordinated Replay Sample Selection for Continual Federated Learning
Continual Federated Learning (CFL) combines Federated Learning (FL), the decentralized learning of a central model on a number of client devices that may not communicate their data, and Continual Learning (CL), the learning of a model from a continual stream of data without keeping the entire history.
Holistic Survey of Privacy and Fairness in Machine Learning
Privacy and fairness are two crucial pillars of responsible Artificial Intelligence (AI) and trustworthy Machine Learning (ML).
Controlling the Extraction of Memorized Data from Large Language Models via Prompt-Tuning
We present a novel approach which uses prompt-tuning to control the extraction rates of memorized content in LLMs.
The Massively Multilingual Natural Language Understanding 2022 (MMNLU-22) Workshop and Competition
Despite recent progress in Natural Language Understanding (NLU), the creation of multilingual NLU systems remains a challenge.
Knowledge Distillation Transfer Sets and their Impact on Downstream NLU Tasks
Yet, the generic corpora used to pretrain the teacher and the corpora associated with the downstream target domain are often significantly different, which raises a natural question: should the student be distilled over the generic corpora, so as to learn from high-quality teacher predictions, or over the downstream task corpora to align with finetuning?
AlexaTM 20B: Few-Shot Learning Using a Large-Scale Multilingual Seq2Seq Model
In this work, we demonstrate that multilingual large-scale sequence-to-sequence (seq2seq) models, pre-trained on a mixture of denoising and Causal Language Modeling (CLM) tasks, are more efficient few-shot learners than decoder-only models on various tasks.
Ranked #14 on
Natural Language Inference
on CommitmentBank
Alexa Teacher Model: Pretraining and Distilling Multi-Billion-Parameter Encoders for Natural Language Understanding Systems
We present results from a large-scale experiment on pretraining encoders with non-embedding parameter counts ranging from 700M to 9. 3B, their subsequent distillation into smaller models ranging from 17M-170M parameters, and their application to the Natural Language Understanding (NLU) component of a virtual assistant system.
Cross-Lingual Natural Language Inference
intent-classification
+5
Differentially Private Decoding in Large Language Models
Recent large-scale natural language processing (NLP) systems use a pre-trained Large Language Model (LLM) on massive and diverse corpora as a headstart.
MASSIVE: A 1M-Example Multilingual Natural Language Understanding Dataset with 51 Typologically-Diverse Languages
We present the MASSIVE dataset--Multilingual Amazon Slu resource package (SLURP) for Slot-filling, Intent classification, and Virtual assistant Evaluation.
Ranked #1 on
Slot Filling
on MASSIVE
Generative Adversarial Networks for Annotated Data Augmentation in Data Sparse NLU
In this paper, we present our results on boosting NLU model performance through training data augmentation using a sequential generative adversarial network (GAN).
Using multiple ASR hypotheses to boost i18n NLU performance
For IC and NER multi-task experiments, when evaluating on the mismatched test set, we see improvements across all domains in German and in 17 out of 19 domains in Portuguese (improvements based on change in SeMER scores).
Abstractive Text Summarization
Automatic Speech Recognition
+10
Using Alternate Representations of Text for Natural Language Understanding
One of the core components of voice assistants is the Natural Language Understanding (NLU) model.
