Uncertainty in large language models
summary
This research theme explores the concept of measuring and managing uncertainty in large language models (LLMs). LLMs are widely used in various applications, from natural language processing to decision-making systems. This research theme discusses the sources of uncertainty in LLMs, including inherent model weaknesses, data quality, and variability in user inputs.
Key areas include:
- Types of uncertainty: to categorize uncertainties into two primary types: aleatoric (inherent variability in data) and epistemic (uncertainty due to model limitations and lack of knowledge).
- Impact on performance: to analyze how these uncertainties can affect the reliability and performance of language models, potentially leading to erroneous outputs in critical applications.
- Quantification methods: to review various methodologies for quantifying uncertainty, such as Bayesian approaches, ensemble methods, and bootstrap techniques, emphasizing the importance of accurately measuring both aleatoric and epistemic uncertainties.
- Applications: to highlight the practical implications of uncertainty quantification in improving model robustness, informing the development of more trustworthy AI systems, and guiding decision-makers in assessing risks. Particularly in healthcare and biomedicine.
- Challenges and future directions: to address the challenges of uncertainty quantification in large-scale models and suggests directions for future research, including developing more efficient algorithms and integrating uncertainty measures into the LLMs' training and deployment processes.
References
- Qiu et al. Semantic Density: Uncertainty Quantification for Large Language Models through Confidence Measurement in Semantic Space. NeurIPS 2024
- Hou et al. Decomposing Uncertainty for Large Language Models through Input Clarification Ensembling. ICML 2024
- Nikitin et al. Kernel Language Entropy: Fine-grained Uncertainty Quantification for LLMs from Semantic Similarities. arXiv preprint arXiv:2405.20003