arxiv_cv 98% Match Research Paper AI Researchers,Machine Learning Engineers,Developers working with LMMs,Data Scientists 1 month ago

Ranked from Within: Ranking Large Multimodal Models Without Labels

large-language-models › evaluation

📄 Abstract

Abstract: Can the relative performance of a pre-trained large multimodal model (LMM) be predicted without access to labels? As LMMs proliferate, it becomes increasingly important to develop efficient ways to choose between them when faced with new data or tasks. The usual approach does the equivalent of giving the models an exam and marking them. We opt to avoid marking and the associated labor of determining the ground-truth answers. Instead, we explore other signals elicited and ascertain how well the models know their own limits, evaluating the effectiveness of these signals at unsupervised model ranking. We evaluate $47$ state-of-the-art LMMs (\eg, LLaVA) across $9$ visual question answering benchmarks, analyzing how well uncertainty-based metrics can predict relative model performance. Our findings show that uncertainty scores derived from softmax distributions provide a robust and consistent basis for ranking models across various tasks. This facilitates the ranking of LMMs on unlabeled data, providing a practical approach for selecting models for diverse target domains without requiring manual annotation.

Key Contributions

Investigates the effectiveness of using uncertainty scores derived from softmax distributions as signals for unsupervised ranking of Large Multimodal Models (LMMs). Demonstrates that these uncertainty scores provide a robust and consistent basis for ranking models across various Visual Question Answering (VQA) tasks, enabling model selection without ground-truth labels.

Business Value

Significantly reduces the cost and effort required to select the best performing LMM for a given task, accelerating development cycles and improving the efficiency of AI deployments. Enables users to choose models without needing extensive labeled evaluation datasets.

Paper Metadata

Innovation Type

Methodological

Deployment Feasibility

The proposed method is a post-hoc evaluation technique that can be applied to existing LMMs, making it highly feasible for practical use in model selection and deployment pipelines.

Limitations Addressed

The difficulty and labor involved in evaluating LMMs using traditional methods that require ground-truth labels; the proliferation of LMMs necessitates efficient selection mechanisms.

Performance Gains

Robust and consistent basis for ranking LMMs across various tasks using uncertainty scores.

Technical Tags

Large Multimodal Models (LMMs)Unsupervised Model RankingUncertainty EstimationSoftmax DistributionsVisual Question Answering (VQA)BenchmarkingModel EvaluationZero-Shot LearningConfidence ScoresModel Selection

Research Topics

Large Language ModelsMultimodal AIModel EvaluationMachine LearningArtificial Intelligence

Methods & Architectures

Uncertainty-based MetricsSoftmax Score AnalysisUnsupervised Model RankingEvaluation across VQA Benchmarks Large Multimodal Models (LMMs)LLaVA (example)

Applications & Tasks

Natural Language Processing Computer Vision AI Model Development Model RankingModel SelectionPerformance EvaluationUnsupervised Learning Ranking Large Multimodal Models (LMMs) without access to labelsPredicting relative model performance using internal signals like uncertainty

Datasets & Benchmarks

Benchmarks

9 Visual Question Answering (VQA) benchmarks

Uncertainty scoresSoftmax distributionsModel ranking accuracy

Related Fields

Model InterpretabilityAI EthicsMachine Learning Operations (MLOps)Natural Language Understanding

Keywords

large multimodal modelsLMMsmodel rankingunsupervised learninguncertainty estimationsoftmaxVQAbenchmarkingmodel evaluationLLaVAconfidencemodel selection

Academic Context

#Large Language Models#Multimodal AI#Model Evaluation#Machine Learning#Artificial Intelligence

Commercial Potential

Potential Products

Automated LMM evaluation and selection toolsModel comparison platformsAPIs for assessing model confidence

Target Industries

TechnologySoftware DevelopmentAI Research

Use Case Examples

Selecting the best LMM for a customer service chatbot without extensive A/B testingComparing different LMMs for image captioning tasks based on their internal confidence

Competitive Edge

Offers a novel, label-free approach to ranking LMMs, providing a more efficient and scalable alternative to traditional evaluation methods that require labeled datasets and extensive testing.

Resource Requirements

Compute Needs

Moderate, for running inference on models across multiple benchmarks.

Data Requirements

Access to various VQA benchmarks and the LMMs to be evaluated.

Deployment Constraints

The effectiveness of uncertainty scores can vary depending on the specific LMM architecture and training.

Scalability

The method is designed to be scalable as it avoids the need for labeled data and relies on internal model signals.

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