IEEE

Multi-Label Text Classification Using Prompting Techniques via LLMs in Software Effort Estimation

Abstract: Text classification (TC) has gained substantial importance across diverse domains due to the increasing proliferation of internet-based and digital technologies. Organizations worldwide ...
EurekAlert!

Lightweight framework boosts cross-domain microseismic signal classification for underground engineering

Struggling with microseismic signal classification in deep underground engineering? Researchers from Sichuan University ...
Frontiers

GECOBench: a gender-controlled text dataset and benchmark for quantifying biases in explanations

Large pre-trained language models have become a crucial backbone for many downstream tasks in natural language processing (NLP), and while they are trained on a plethora of data containing a variety ...
Frontiers

Parameter-efficient fine-tuning for low-resource text classification: a comparative study of LoRA, IA

The successful application of large-scale transformer models in Natural Language Processing (NLP) is often hindered by the substantial computational cost and data requirements of full fine-tuning.
Scientific Research Publishing

Layer Cake: On Language Representation and Compute Characteristics in Text Classification ()

Since transformer-based language models were introduced in 2017, they have been shown to be extraordinarily effective across a variety of NLP tasks including but not limited to language generation.
IEEE

TransFINN “Transparent Feature Integrated Neural Network for Text Feature Selection and Classification”

Abstract: This paper introduces TransFINN, a transparent extension artificial neural network that combines transparent feature selection with text classification. TransFINN provides clear insights ...