TeTIm-Eval: A Novel Curated Evaluation Data Set for Comparing Text-to-Image Models

Galatolo, Federico A.; Cimino, Mario G. C. A.; Edoardo, Cogotti,

doi:10.5220/0011885800003411

Cited by 4 publications

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The Language-Agnostic BERT Sentence Embedding (LaBSE) [37][38][39] model represents a significant advancement in the field of Natural Language Processing (NLP), particularly in the realm of creating language-agnostic sentence embeddings. Developed and released by Google, LaBSE is designed to efficiently produce high-quality embeddings for sentences across multiple languages, making it an invaluable tool in globalized and multilingual applications.…”

Section: The Language-agnostic Bert Sentence Embedding (Labse) Modelmentioning

confidence: 99%

Zero-Shot Recommendation AI Models for Efficient Job–Candidate Matching in Recruitment Process

Kurek,

Latkowski,

Bukowski

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

In the evolving realities of recruitment, the precision of job–candidate matching is crucial. This study explores the application of Zero-Shot Recommendation AI Models to enhance this matching process. Utilizing advanced pretrained models such as all-MiniLM-L6-v2 and applying similarity metrics like dot product and cosine similarity, we assessed their effectiveness in aligning job descriptions with candidate profiles. Our evaluations, based on Top-K Accuracy across various rankings, revealed a notable enhancement in matching accuracy compared to conventional methods. Specifically, the all-MiniLM-L6-v2 model with a chunk length of 768 exhibited outstanding performance, achieving a remarkable Top-1 accuracy of 3.35%, 55.45% for Top-100, and an impressive 81.11% for Top-500, establishing it as a highly effective tool for recruitment processes. This paper presents an in-depth analysis of these models, providing insights into their potential applications in real-world recruitment scenarios. Our findings highlight the capability of Zero-Shot Learning to address the dynamic requirements of the job market, offering a scalable, efficient, and adaptable solution for job–candidate matching and setting new benchmarks in recruitment efficiency.

show abstract

Section: The Language-agnostic Bert Sentence Embedding (Labse) Modelmentioning

confidence: 99%

Zero-Shot Recommendation AI Models for Efficient Job–Candidate Matching in Recruitment Process

Kurek,

Latkowski,

Bukowski

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

show abstract

Image-Based Screening of Oral Cancer via Deep Ensemble Architecture

Parola,

Mantia,

Galatolo

et al. 2023

2023 IEEE Symposium Series on Computational Intelligence (SSCI)

View full text Add to dashboard Cite

L-VEIGe: Development of L2 Vocabulary Learning Support System with Suggestibility of Error by Image Generation

Sugita,

Gu,

Ota

et al. 2024

Preprint

View full text Add to dashboard Cite

Vocabulary learning has long been taught as a basis for L2 English language learning. The use of visual clues is a widely recognized method in vocabulary learning. Several research have applied automatic image caption generation, invented with computer vision and natural language processing, to English vocabulary learning. However, these vocabulary learning systems mainly use correct answers and their corresponding images. On the other hand, in English vocabulary learning, Fossilization is a problem where errors become difficult to correct and become established through repeated errors by learners. While the use of images has been effective in traditional English vocabulary learning, there has been little research focusing on learners' incorrect answers. In this research, we intentionally created situations where learners are likely to give incorrect answers and constructed an English vocabulary learning support system L-VEIGe (Learning-Vocabulary Error Image Generation) that generates images in response to learners' incorrect answers to promote effective introspection and eliminate repeated errors. An evaluation experiment targeting graduate students who are second language English learners revealed that a proposed method effectively prevents repetitive errors compared to a method without image generation.

show abstract

TeTIm-Eval: A Novel Curated Evaluation Data Set for Comparing Text-to-Image Models

Cited by 4 publications

References 0 publications

Zero-Shot Recommendation AI Models for Efficient Job–Candidate Matching in Recruitment Process

Zero-Shot Recommendation AI Models for Efficient Job–Candidate Matching in Recruitment Process

Image-Based Screening of Oral Cancer via Deep Ensemble Architecture

L-VEIGe: Development of L2 Vocabulary Learning Support System with Suggestibility of Error by Image Generation

Contact Info

Product

Resources

About