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GPT3Mix: Leveraging large-scale language models for text augmentation
Large-scale language models such as GPT-3 are excellent few-shot learners, allowing them
to be controlled via natural text prompts. Recent studies report that prompt-based direct …
to be controlled via natural text prompts. Recent studies report that prompt-based direct …
Federated social recommendation with graph neural network
Recommender systems have become prosperous nowadays, designed to predict users'
potential interests in items by learning embeddings. Recent developments of the Graph …
potential interests in items by learning embeddings. Recent developments of the Graph …
Augmenting sequential recommendation with pseudo-prior items via reversely pre-training transformer
Sequential Recommendation characterizes the evolving patterns by modeling item
sequences chronologically. The essential target of it is to capture the item transition …
sequences chronologically. The essential target of it is to capture the item transition …
Mixup-transformer: Dynamic data augmentation for NLP tasks
Mixup is the latest data augmentation technique that linearly interpolates input examples
and the corresponding labels. It has shown strong effectiveness in image classification by …
and the corresponding labels. It has shown strong effectiveness in image classification by …
State-of-the-art generalisation research in NLP: a taxonomy and review
The ability to generalise well is one of the primary desiderata of natural language
processing (NLP). Yet, what'good generalisation'entails and how it should be evaluated is …
processing (NLP). Yet, what'good generalisation'entails and how it should be evaluated is …
Few-shot intent detection via contrastive pre-training and fine-tuning
In this work, we focus on a more challenging few-shot intent detection scenario where many
intents are fine-grained and semantically similar. We present a simple yet effective few-shot …
intents are fine-grained and semantically similar. We present a simple yet effective few-shot …
Crafting clarity: Leveraging large language models to decode consumer reviews
Abstract Large Language Models (LLMs) have emerged as powerful tools for understanding
consumer perceptions and extracting insights from unstructured textual data. This study …
consumer perceptions and extracting insights from unstructured textual data. This study …
Incremental few-shot text classification with multi-round new classes: Formulation, dataset and system
Text classification is usually studied by labeling natural language texts with relevant
categories from a predefined set. In the real world, new classes might keep challenging the …
categories from a predefined set. In the real world, new classes might keep challenging the …
Effectiveness of pre-training for few-shot intent classification
This paper investigates the effectiveness of pre-training for few-shot intent classification.
While existing paradigms commonly further pre-train language models such as BERT on a …
While existing paradigms commonly further pre-train language models such as BERT on a …
Fine-tuning pre-trained language models for few-shot intent detection: Supervised pre-training and isotropization
It is challenging to train a good intent classifier for a task-oriented dialogue system with only
a few annotations. Recent studies have shown that fine-tuning pre-trained language models …
a few annotations. Recent studies have shown that fine-tuning pre-trained language models …