loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Affiliation

Department of Informatics, PUC-Rio, Marquês de São Vicente, 225 RDC, 4th floor - Gávea, Rio de Janeiro, Brazil
Context (please use complete words only)
Field
Sort by

2 Papers

A Data Annotation Approach Using Large Language Models

Topics: Industrial Applications of Artificial Intelligence; Natural Language Interfaces to Intelligent Systems; Deep Learning

Authors: Carlos Rocha , Jonatas Grosman , Fernando Correia , Venicius Rego and Hélio Lopes

Affiliation: Department of Informatics, PUC-Rio, Marquês de São Vicente, 225 RDC, 4th floor - Gávea, Rio de Janeiro, Brazil

Digital Object Identifier: 10.5220/0013280100003929

Proceedings of ICEIS 2025, ISBN: 978-989-758-749-8

Collapse Button Quick Abstract
Documents are crucial for the economic and academic systems, yet extracting information from them can be complex and time-consuming. Visual Question Answering (VQA) models address this challenge using natural language prompts to extract information. However, their development depends on annotated datasets, which are costly to produce. To face this challenge, we propose a four-step process that combines Computer Vision Models and Large Language Models (LLMs) for VQA data annotation in financial reports. This method starts with Document Layout Analysis and Table Structure Extraction to identify document structures. Then, it uses two distinct LLMs for the generation and evaluation of question and answer pairs, automating the construction and selection of the best pairs for the final dataset. As a result, we found Mixtral-8x22B and GPT-4o mini to be the most cost-benefit for generating pairs, while Claude 3.5 Sonnet performed best for evaluation, aligning closely with human assessments.

Zero-Shot Product Description Generation from Customers Reviews

Topics: Industrial Applications of Artificial Intelligence; Natural Language Interfaces to Intelligent Systems

Authors: Bruno Gutierrez , Jonatas Grosman , Fernando Correia and Hélio Lopes

Affiliation: Department of Informatics, PUC-Rio, Marquês de São Vicente, 225 RDC, 4th floor - Gávea, Rio de Janeiro, Brazil

Digital Object Identifier: 10.5220/0013280300003929

Proceedings of ICEIS 2025, ISBN: 978-989-758-749-8

Collapse Button Quick Abstract
In e-commerce, product descriptions have a great influence on the shopping experience, informing consumers and facilitating purchases. However, creating good descriptions is labor-intensive, especially for large retailers managing daily product launches. To address this, we propose an automated method for product description generation using customer reviews and a Large Language Model (LLM) in a zero-shot approach. Our three-step process involves (i) extracting valuable sentences from reviews, (ii) selecting informative and diverse content using a graph-based strategy, and (iii) generating descriptions via prompts based on these selected sentences and the product title. For our proposal evaluation, we had the collaboration of 30 evaluators comparing the generated descriptions with the ones given by the sellers. As a result, our method produced descriptions preferred over those provided by sellers, rated as more informative, readable, and relevant. Additionally, a comparison with a literature method demonstrated that our approach, supported by statistical testing, results in more effective and preferred descriptions.

Topic(s):

Filter listed results, by

Author
Locate Author in list below
Add Author(s) to filter:

- Science and Technology Publications, Lda.
RESOURCES

Proceedings

Papers

Authors

Ontology

CONTACTS

Science and Technology Publications, Lda
Avenida de S. Francisco Xavier, Lote 7 Cv. C,
2900-616 Setúbal, Portugal.

Phone: +351 265 520 185 (National fixed network call)
Fax: +351 265 520 186
Email: info@scitepress.org

EXTERNAL LINKS

PRIMORIS

INSTICC

SCITEVENTS

CROSSREF

PROCEEDINGS SUBMITTED FOR INDEXATION BY:

dblp

Ei Compendex

SCOPUS

Semantic Scholar

Google Scholar

Microsoft Academic