mlsanityData to 7 Oct 2026

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

arXiv 2504.04462 ↗First posted 6 Apr 2025cs.CL cs.AIcited by 15

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Abstract (arXiv)

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 analyze the core causes of MVP, presenting illustrative examples and a case study to highlight its impact. In addition, we investigate key challenges and mitigation strategies, paying particular attention to the role of temperature as a driver of output randomness and emphasizing 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-stakes domains such as finance, healthcare, and policymaking, among others.

Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). Builds on 22 of its 40 references that are in the map. First posted on arXiv 6 Apr 2025.

Builds on

22 of 40 references.

References · 22

  1. 2019Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AIcited by 1,543
  2. 2023Explainability for Large Language Models: A Surveycited by 331
  3. 2023Sentiment Analysis in the Era of Large Language Models: A Reality Checkcited by 262
  4. 2024Benchmarking LLMs via Uncertainty Quantificationcited by 124
  5. 2023Explainable Artificial Intelligence (XAI) 2.0: A Manifesto of Open Challenges and Interdisciplinary Research Directionscited by 123
  6. 2024A Survey on Uncertainty Quantification of Large Language Models: Taxonomy, Open Research Challenges, and Future Directionscited by 115
  7. 2020Predicting trends in the quality of state-of-the-art neural networks without access to training or testing datacited by 113
  8. 2024From Understanding to Utilization: A Survey on Explainability for Large Language Modelscited by 47
  9. 2024LLM Voting: Human Choices and AI Collective Decision Makingcited by 47
  10. 2024A Taxonomy for Human-LLM Interaction Modes: An Initial Explorationcited by 38
  11. 2023Exploring the Sensitivity of LLMs' Decision-Making Capabilities: Insights from Prompt Variation and Hyperparameterscited by 32
  12. 2023Generative AI Text Classification using Ensemble LLM Approachescited by 29
10 more
  1. 2024CompactifAI: Extreme Compression of Large Language Models using Quantum-Inspired Tensor Networkscited by 29
  2. 2024MiniPLM: Knowledge Distillation for Pre-Training Language Modelscited by 27
  3. 2025Understanding the Uncertainty of LLM Explanations: A Perspective Based on Reasoning Topologycited by 24
  4. 2025An Overview of Large Language Models for Statisticianscited by 24
  5. 2025An Empirical Analysis of Uncertainty in Large Language Model Evaluationscited by 20
  6. 2024Large Language Models Meet Text-Centric Multimodal Sentiment Analysis: A Surveycited by 16
  7. 2025Language Agents as Digital Representatives in Collective Decision-Makingcited by 15
  8. 2024Comparative Analysis of Pooling Mechanisms in LLMs: A Sentiment Analysis Perspectivecited by 12
  9. 2025Uncertainty Quantification and Decomposition for LLM-based Recommendationcited by 12
  10. 2024Rethinking the Uncertainty: A Critical Review and Analysis in the Era of Large Language Modelscited by 6

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Citing papers per year: 2025: 4; 2026: 11.

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  2. 2025Robustness in Large Language Models: A Survey of Mitigation Strategies and Evaluation Metricscited by 12
  3. 2025Failure Modes in LLM Systems: A System-Level Taxonomy for Reliable AI Applicationscited by 9
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  9. 2026Uncertainty and Fairness Awareness in LLM-Based Recommendation Systemscited by 1
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