ReviewFrontiers in artificial intelligence2025
An overview of model uncertainty and variability in LLM-based sentiment analysis: challenges, mitigation strategies, and the role of explainability.
Review in Frontiers in artificial intelligence, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Quality of Life of People Living With Dementia Residing in Nursing Homes: Secondary Analysis of Observational Data.Journal of medical Internet research · 2026Article
- Reliable Hardware Sensor and Large Language Model Fusion for Intelligent Short-Term Market Risk Sensing and Prediction.Sensors (Basel, Switzerland) · 2026Article
- Performance Comparison of a Domain-Specific Chatbot and General-Purpose Chatbots in Dental Traumatology.Dental traumatology : official publication of International Association for Dental Traumatology · 2026Article
- Evaluating Elicit's systematic reviews workflow in an umbrella review on air pollution and acute lower respiratory infections: a methodological study for quality appraisal.BMC medical research methodology · 2026Article
- A two-stage generative-AI fusion intrusion detection system for calibrated and reliable cloud security.Scientific reports · 2026Article
- Same Prompt, Different Answer: Why AI Disagrees with Itself.Journal of general internal medicine · 2026Article
- Effect of large language model assistance on undergraduate art history question-answering performance: a randomized crossover pilot study.Frontiers in psychology · 2026Article
- Role reconstruction among double-qualified nursing educators in the generative AI era: a qualitative study.Frontiers in medicine · 2026Article
- Exploring the application of large language models in coding the experiencing scale (EXP).Cogent mental health · 2026Article
- Artificial Intelligence in Plastic Surgery Education: A Global Multimodel Benchmark of Large Language Models on the Plastic Surgery In-Service Training Examination.Aesthetic surgery journal. Open forum · 2026Article
- Transformer and Pre-Transformer Model-Based Sentiment Prediction with Various Embeddings: A Case Study on Amazon Reviews.Entropy (Basel, Switzerland) · 2025Article
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Authors and funding
7 authors.
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Abstract
Large Language Models (LLMs) have significantly advanced sentiment analysis, yet their inherent uncertainty and variability pose critical challenges to achieving reliable and consistent outcomes. This paper systematically explores the Model Variability Problem (MVP) in LLM-based sentiment analysis, characterized by inconsistent sentiment classification, polarization, and uncertainty arising from stochastic inference mechanisms, prompt sensitivity, and biases in training data. We present illustrative examples and two case studies to highlight its impact and analyze the core causes of MVP, discussing a dozen fundamental reasons for model variability. We pay especial atenttion to explainabily, with an analysis of its importance in LLMs from the MVP perspective. In addition, we investigate key challenges and mitigation strategies, paying particular attention to the role of temperature as a driver of output randomness and highlighting the crucial role of explainability in improving transparency and user trust. By providing a structured perspective on stability, reproducibility, and trustworthiness, this study helps develop more reliable, explainable, and robust sentiment analysis models, facilitating their deployment in high-risk domains such as finance, healthcare and policy making, among others.
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