A Comprehensive Survey of Confidence Estimation and Calibration in Large Language Models

Core Concepts

Confidence estimation and calibration are crucial for improving the reliability of Large Language Models by addressing errors and biases.

Abstract

This survey explores confidence estimation and calibration in Large Language Models (LLMs). It covers fundamental concepts, challenges, methods, applications, and future directions. The content is structured as follows:

Introduction to Confidence Estimation and Calibration in LLMs.
Preliminaries and Background covering Basic Concepts, Metrics, and Methods.
White-Box Methods for Confidence Estimation including Logit-based methods, Internal state-based methods, Semantics-based methods.
Black-Box Methods for Confidence Estimation including Linguistic confidence methods, Consistency-based estimation, Surrogate models.
Calibration Methods for improving generation quality and linguistic confidence.
Applications such as Hallucination detection, Ambiguity detection, Uncertainty-guided data exploitation.
Future Directions focusing on Comprehensive Benchmarks, Multi-modal LLMs, Calibration to human variation.

Stats

"Large language models (LLMs) have demonstrated remarkable capabilities across a wide range of tasks in various domains."
"Confidence (or uncertainty) estimation is crucial for tasks like out-of-distribution detection and selective prediction."
"The output space of these models is significantly larger than that of discriminative models."
"Model calibration focuses on aligning predictive probabilities to actual accuracy."

Quotes

"Language models are few-shot learners." - Tom B. Brown et al., 2020b
"Uncertainty in natural language generation: From theory to applications." - Joris Baan et al., 2023

Key Insights Distilled From

A Survey of Confidence Estimation and Calibration in Large Language Models

by Jiahui Geng,... at arxiv.org 03-26-2024

https://arxiv.org/pdf/2311.08298.pdf

Deeper Inquiries

How can confidence estimation techniques be adapted for multimodal large language models?

Confidence estimation techniques for multimodal large language models (MLLMs) can be adapted by incorporating both textual and visual cues into the confidence assessment process. Since MLLMs combine text and image inputs, the confidence estimation should consider the uncertainty associated with both modalities. One approach is to develop hybrid methods that leverage information from both modalities to estimate overall model confidence accurately.
Additionally, calibration methods specific to each modality can be employed to ensure that the model's predictions align well with ground truth labels in both textual and visual domains. By calibrating each modality separately and then integrating them appropriately, MLLMs can provide more reliable predictions across different tasks.
Furthermore, leveraging surrogate models trained on multimodal data can help in estimating the confidence of MLLMs effectively. These surrogate models can mimic the behavior of MLLMs when provided with combined text-image inputs, enabling better understanding of how confident or uncertain the MLLM is in its predictions.
In summary, adapting confidence estimation techniques for MLLMs involves considering uncertainties from multiple modalities, developing hybrid approaches that integrate textual and visual information, employing modality-specific calibration methods, and utilizing surrogate models trained on multimodal data for accurate confidence assessment.

How do human variations impact the calibration of Large Language Models?

Human variation has significant implications on the calibration of Large Language Models (LLMs) as it introduces subjectivity and inconsistency in labeling data used for training and evaluation. The diverse ways in which humans interpret tasks or provide annotations lead to discrepancies in ground truth labels assigned to LLMs during training.
The presence of human variation affects LLM calibration by introducing biases related to annotators' subjective judgments or differing interpretations of ambiguous tasks. This variability results in misalignments between LLM prediction distributions and human disagreement levels during inference.
To address these challenges posed by human variation on LLM calibration:

Accounting for Ambiguity: Calibration methods need to account for ambiguity present in labeled datasets due to varying interpretations among annotators.
Robustness Testing: Robustness testing against different types of ambiguity scenarios introduced by human variation is crucial.
Model Adaptation: Developing adaptive calibration strategies that adjust based on observed inconsistencies arising from human variations.
Data Augmentation: Introducing augmented datasets reflecting diverse perspectives helps improve model robustness against varied annotations.

By acknowledging and mitigating the effects of human variation through tailored calibration strategies sensitive to subjective differences among annotators, LLMs can achieve improved reliability across a range of applications.

How can ambiguity detection techniques be improved using confidence estimation?

Improving ambiguity detection techniques using confidence estimation involves leveraging uncertainty measures derived from model predictions to identify instances where a model may struggle due to ambiguous input or conflicting signals within data samples:

Thresholding Confidence Scores: Setting thresholds based on predicted confidences allows distinguishing between clear-cut responses and potentially ambiguous ones.

Consistency Analysis: Utilizing consistency-based metrics such as inter-model agreement or intra-model coherence helps detect ambiguities where models exhibit varying degrees of certainty across similar inputs.

Prompt Variations: Employing diverse prompts or question formulations aids in triggering nuanced responses from language models that reveal their level of certainty regarding an answer’s correctness.

4 .Ensemble Methods: Aggregating outputs from multiple ensemble members provides insights into areas where individual members disagree due to inherent ambiguities present within input samples.
5 .Fine-tuning Strategies: Fine-tuning LLMs specifically towards detecting ambiguity through targeted loss functions focused on minimizing errors caused by unclear instances enhances their ability at discerning uncertain cases effectively.
By integrating these approaches alongside sophisticated modeling techniques like linguistic analysis coupled with consistency checks driven by estimated confidences enables more robust identification and handling of ambiguous scenarios within Large Language Models (LLMs).