Logo Lanfrica
  • Accueil
  • Atlas
  • Analyses
  • Documentation
  • Sign in

© 2026 Lanfrica. Tous droits réservés. Tous les droits d'auteur des ressources affichées sur le site Web Lanfrica appartiennent aux détenteurs de droits d'auteur d'origine, sauf indication contraire explicite.

COMPARISON OF ZERO AND FEW-SHOT LEARNING APPROACH USING THE LLMS FOR SENTIMENT ANALYSIS IN SERBIAN LITERATURE

Domaine:

natural language processing

Type de record:

paper
Créateur:
PetIkoŠkoSta
Éditeur:
Zenodo
Hôte:avatar
Goal: Assess if newer versions of LLMs offer more consistent, efficient, and potentially less biased sentiment annotation.Comparison: With previous research on srpELTeC Sentiment Analysis employing Mistral 7B model.Impact: Advance sentiment data processing and interpretation across various applications.Significance for Low-Resource Languages: Highlight challenges and benefits for languages like Serbian, where annotated corpora are scarce, limiting traditional sentiment analysis models.

Visit

doi.orgzenodo.org

Tasks

sentiment analysistext classification

Licenses

Creative Commons Attribution 4.0 Internationalhttps://creativecommons.org/licenses/by/4.0/legalcode

Similaires

Parameter-Efficient Few-Shot Sentiment Analysis Using LoRA-Enhanced TransformersZero-shot Sentiment Analysis in Low-Resource Languages Using a Multilingual Sentiment LexiconIntermediate-Task Training and Few-Shot Learning for Cross-Domain Robustness in Zero-Shot Cross-Lingual TransferCross-lingual NER Annotation Projection vs. Zero-Shot Few-Shot Learning in Low-Resource Languageskobeoseijnr/Few-Shot-Sentiment-Learning-with-Synthetic-Augmentation-in-Low-Resource-SettingsComparative Study of Different Learning Paradigms for Zero-Shot Sentiment Analysis of the Low-Resource African Language Oromo

Parameter-Efficient Few-Shot Sentiment Analysis Using LoRA-Enhanced Transformers

Sentiment analysis in low-resource languages is often limited by scarce annotated data and the high

Zero-shot Sentiment Analysis in Low-Resource Languages Using a Multilingual Sentiment Lexicon

Improving multilingual language models capabilities in low-resource languages is generally difficult

Intermediate-Task Training and Few-Shot Learning for Cross-Domain Robustness in Zero-Shot Cross-Lingual Transfer

Pre-trained multilingual language encoders, such as multilingual BERT and XLM-R, show great potentia

Cross-lingual NER Annotation Projection vs. Zero-Shot Few-Shot Learning in Low-Resource Languages

Cross-lingual Named Entity Recognition (NER) leverages knowledge transfer between languages to ident

kobeoseijnr/Few-Shot-Sentiment-Learning-with-Synthetic-Augmentation-in-Low-Resource-Settings

Few Shot Sentiment Learning with Synthetic Augmentation in Low Resource Settings # Few-Shot Sentime

Comparative Study of Different Learning Paradigms for Zero-Shot Sentiment Analysis of the Low-Resource African Language Oromo

In this paper, we address zero-shot sentiment analysis for Oromo, a low-resource language spoken in