This focuses on computational approaches to understanding, processing, and generating human languages.
The paper discusses CREMA, a modality-fusion framework aimed at enhancing the efficiency of multimodal compositional video reasoning. This framework is designed to integrate any new modality into video reasoning, leveraging…
Continue readingThe article introduces SPHINX-X, a Multi-modality Large Language Model (MLLM) series, which is an enhancement of the SPHINX framework. The researchers have improved the architecture and training efficiency by removing…
Continue readingThe study aimed to identify machine learning models that could efficiently categorize tweets concerning eating disorders. Over a million tweets were collected over three months and classified using both traditional…
Continue readingThis research focuses on understanding the information encoded in speech processing by using vector representations of speech from a pretrained model. The study proposes an unsupervised method using ABX tests…
Continue readingThe paper discusses the challenges in text data augmentation due to the discrete nature of sentences. It highlights the limitations of rule-based augmentation methods and softEDA (easy data augmentation with…
Continue readingThis paper introduces SoftEDA, a novel technique that applies soft labels to augmented data. Traditional Easy Data Augmentation (EDA) techniques can potentially damage the original meaning of the text, hurting…
Continue readingThe paper discusses AttnLRP, a method that extends Layer-wise Relevance Propagation to handle attention layers in transformer models. It aims to provide better understanding of the reasoning process of these…
Continue readingThis research proposes using small pretrained foundational generative language models as a general learning framework for sequence-based tasks. The approach overcomes challenges associated with training neural networks and language models…
Continue readingThis academic article presents a comprehensive survey on the use of deep learning methodologies for skill extraction and classification from job postings. The paper addresses the lack of standardized terminology…
Continue readingThe article presents a study from IBM Research AI on creating efficient models for detecting hate, abuse, and profanity (HAP) in Large Language Models (LLMs). The authors highlight the importance…
Continue readingLooks like you're using an ad blocker. Please support us by disabling these ad blocker.
