cushLEPOR: customising hLEPOR metric using Optuna for higher agreement with human judgments or pre-trained language model LaBSE

Han, Lifeng; Sorokina, Irina; Erofeev, Gleb; Gladkoff, Serge

doi:10.48550/arxiv.2108.09484

Cited by 1 publication

(1 citation statement)

References 13 publications

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“…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

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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.

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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

cushLEPOR: customising hLEPOR metric using Optuna for higher agreement with human judgments or pre-trained language model LaBSE

Cited by 1 publication

References 13 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

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