Uncertainty quantification in fine-tuned LLMs using LoRA ensembles

Abstract: Fine-tuning LLMs can improve task specific performance, although a general understanding of what the fine-tuned model has learned, forgotten and how to trust its predictions is still missing. We derive principled uncertainty quantification for fine-tuned LLMs with posterior approximations using computationally efficient low-rank adaptation ensembles. We analyze three common multiple-choice datasets using low-rank adaptation ensembles based on Mistral-7b, and draw quantitative and qualitative conclusions on their perceived complexity and balance between retained prior knowledge and domain specific adaptation during and after fine-tuning. We identify unexpected retention of acquired knowledge during fine-tuning in the overfitting regime.

• The paper introduces a novel framework for quantifying uncertainty in fine-tuned LLMs using LoRA ensembles and Bayesian deep learning.
• It employs low-rank adaptation to mitigate overfitting and enhance computational efficiency while measuring uncertainties with predictive entropy and mutual information.
• Results show improved calibration and AUROC metrics, offering valuable insights into dataset complexity and model robustness.

Uncertainty Quantification in Fine-Tuned LLMs Using LoRA Ensembles

Introduction

The paper explores uncertainty quantification (UQ) in fine-tuned LLMs utilizing Low-Rank Adaptation (LoRA) ensembles. As LLMs adapt to specialized domains through fine-tuning, assessing and quantifying uncertainty becomes paramount, especially to understand the areas of knowledge these models grasp, retain, or find challenging. By leveraging LoRA, a parameter-efficient technique, the paper explores ensemble methods for posterior approximation, critiquing their efficacy using multiple-choice datasets.

Methodology

The authors employ Bayesian Deep Learning, guiding the fine-tuning of LLMs with UQ in mind. They initiate by transforming the LLM fine-tuning challenge into deriving posterior approximations within Bayesian frameworks using ensemble models. LoRA is employed to reduce the parameters adjusted during fine-tuning, optimizing computational and memory efficiency. Key techniques introduced include measuring entropic uncertainty via predictive entropy and mutual information, distinguishing aleatoric and epistemic uncertainties in Bayesian models.

Results

The experimental results are centered around LoRA ensembles trained on datasets like CommonsenseQA (CQA), MMLU STEM, and MMLU Social Sciences. The ensembles, consisting of five members (M=5), address dataset complexity and architecture efficacy using entropic measures.

Figure 1: Performance of the LoRA ensembles trained and evaluated on either CQA, MMLU STEM, or MMLU SS dataset.

The LoRA ensembles showcase a noticeable reduction in overfitting compared to single-model approaches. This reduction is evident in the loss and expected calibration error trends. The study identifies how the ensemble approach leads to different confidence levels across predictions, underlining the role of epistemic uncertainty in perceived dataset complexity and out-of-domain behavior.

AUROC metrics are utilized to quantify model performance in distinguishing in-domain from out-of-domain data. Notably, models trained on CQA exhibit higher AUROC values when evaluated against MMLU datasets, reflecting the inherent difficulty these pose to the architectures.

Figure 2: AUROC computed for the CQA, MMLU STEM, and MMLU Social Studies datasets.

Analysis of Uncertainty

Through an exploration of entropy and mutual information histograms, the paper uncovers significant insights into uncertainty quantification. For instance, high predictive entropy with low mutual information in some datasets pinpoints shortcomings in the model's architecture when handling certain complexities, even post fine-tuning.

Figure 3: Histograms of predictive entropy and mutual information for a LoRA ensemble trained and evaluated on the CQA dataset.

The paper affirms that high epistemic uncertainty signals difficult-to-learn domains, whereas overfitting symptoms manifest as increased entropy over epochs for incorrect predictions. This analysis is crucial for understanding both architectural limitations and the inherent complexity of target datasets.

Conclusions

The work presents a novel framework through which uncertainty can be efficiently approximated in fine-tuned LLMs using LoRA ensembles. By prioritizing Bayesian interpretations and entropic uncertainty measures, the paper significantly contributes to understanding dataset complexity and domain applicability for LLMs. Furthermore, the efficient parameter adaptation via LoRA augments the computational feasibility of deploying these models in practical settings.

Future research paths could extend this work by exploring alternative ensemble compositions or by refining priors to further calibrate models for uncertainty in increasingly complex tasks. These insights are vital for deploying LLMs in high-stakes environments where understanding uncertainty is as critical as achieving accuracy itself.