most citedAn overview of model uncertainty and variability in LLM-based sentiment analysis. Challenges, mitigation strategies and the role of explainability

2 citations · 3 across the 4 of their papers we have counts for

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cs.CL2025

A Domain-Based Taxonomy of Jailbreak Vulnerabilities in Large Language Models

Carlos Peláez-González, Andrés Herrera-Poyatos, Cristina Zuheros +3

The study of large language models (LLMs) is a key area in open-world machine learning. Although LLMs demonstrate remarkable natural language processing capabilities, they also fac…

cs.CL2025★ 2 cited

An overview of model uncertainty and variability in LLM-based sentiment analysis. Challenges, mitigation strategies and the role of explainability

David Herrera-Poyatos, Carlos Peláez-González, Cristina Zuheros +4

Large Language Models (LLMs) have significantly advanced sentiment analysis, yet their inherent uncertainty and variability pose critical challenges to achieving reliable and consi…