LMMs-Eval: Multimodal Model Evaluation

• LMMs-Eval is a comprehensive benchmarking framework that standardizes the evaluation of large multimodal models across over 50 diverse tasks.
• It integrates unified pipelines, lightweight coreset toolkits, and dynamic live benchmarking to balance evaluation coverage, cost-efficiency, and contamination minimization.
• The framework enables reproducible assessments, transparent leaderboard reporting, and rapid ablation studies for fair multimodal model comparisons.

Large Multimodal Model Evaluation (“LMMs-Eval”) refers to a family of comprehensive frameworks, datasets, and methodologies for standardized benchmarking of large multimodal models (LMMs) across a broad spectrum of tasks, model types, and operational concerns. With the proliferation of LMMs in research and industry, coverage, cost-efficiency, and dataset contamination have become principal considerations for reproducible assessment. “LMMs-Eval” encompasses unified pipelines, pruned lightweight toolkits, and continuous live-benchmarking to address the so-called evaluation trilemma: maximizing coverage, minimizing evaluation cost, and eliminating training/test contamination. The framework supports systematic comparisons across open and closed-source models, automated task-selection, metric standardization, and dynamic leaderboard reporting, establishing itself as a reference for reliable and scalable LMM evaluation (Zhang et al., 17 Jul 2024).

1. Framework Architecture and Coverage

LMMs-Eval is architected as an end-to-end pipeline comprising data preprocessing, a unified model inference interface (supporting both API and local evaluation), metrics standardization, and structured logging. The framework is designed for the simultaneous evaluation of multiple LMM variants across more than 50 diverse multimodal tasks, such as image captioning, document and infographic understanding (e.g., DocVQA, ChartQA), visual question answering (VQA), mathematical and science reasoning (MathVista, ScienceQA), and multi-disciplinary real-world benchmarks.

A summary of representative coverage in LMMs-Eval is illustrated below:

Category	Example Tasks	Evaluation Aspect
Image Captioning	Flickr30k, NoCaps, TextCaps	Semantic Fidelity
Visual Reasoning	VQA, OKVQA, RefCOCO	Contextual Comprehension
Document Understanding	DocVQA, ChartQA	Stratified Structure
STEM Reasoning	MathVista, ScienceQA	Logical + Visual

The system supports evaluation of more than 10 major LMMs (yielding ~30 sub-variants), including LLaVA-series (7B–110B), InstructBLIP, InternVL, Qwen-VL, commercial APIs (GPT-4, Claude, Gemini), and others.

2. Addressing the Evaluation Trilemma: Coverage, Cost, and Contamination

The core challenge (“evaluation trilemma”) arises from the inability to simultaneously maximize task/model coverage, minimize computational cost, and guarantee zero contamination of training with evaluation data. LMMs-Eval addresses this via:

• Full Suite: Exhaustive coverage with the entire task battery, trading increased runtime and possible overlap with training data for maximum reliability and transparency.
• LMMs-Eval Lite: A pruned, efficiency-oriented toolkit that selects representative “coresets” from each task using a k-center greedy algorithm. This coreset selection is formally:

$$\min_{V: |V| \leq |D|} \left| \frac{1}{|D|} \sum_{i=1}^{|D|} S(y_i, \hat{y}_i) - \frac{1}{|V|} \sum_{i=1}^{|V|} S(y_i, \hat{y}_i) \right|$$

where $S$ is the scoring function, $|D|$ is the full set, and $|V|$ the coreset. This is NP-hard but well-approximated via greedy selection and CLIP/BGE-M3 embedding-based clustering. Correlations exceeding $r>0.9$ between coreset and full-set scores are routinely reported, minimizing evaluation time while preserving metric fidelity.

• Multimodal LiveBench: A dynamically updating “live” benchmark that harvests data from online sources (news, forums, social media) to create questions and evaluation samples that are by construction out-of-distribution and contamination-free. The pipeline involves content extraction, question generation (using a leading multimodal model), human and model-based review, and continuous leaderboard scoring.

3. Technical Implementation and Aggregation Strategies

LMMs-Eval manages model inference via unified APIs, enabling both open/free and commercial/closed models. All outputs are scored using standardized metrics appropriate to each task (e.g., BLEU for captioning, accuracy for classification, WUPS for VQA, etc.). Every output, prompt, and intermediate result is logged with metadata supporting empirical reproducibility.

• Metric Aggregation: For the Lite toolkit, the coreset is selected to maximize score representativity per task. Scores from multiple models and datasets are aggregated to produce final rankings, and the relative performance between Lite and Full is continuously monitored.
• Leaderboard Reporting: Outputs and scores are reported on a public leaderboard (HuggingFace Spaces), supporting tracking over time and community participation.

4. Practical Solutions and Trade-Offs

The trilemma cannot be perfectly solved, but practical trade-offs are provided:

• Comprehensive runs (Full) ensure maximal confidence at elevated computational and time cost; Lite is suited for ablation, fast development cycles, and low-resource environments.
• For contamination minimization, LiveBench uses only data released after the cutoff date for model training, and every evaluation instance is unique to each run—this ensures true zero-contamination but requires continuous dataset maintenance and question validation.

These complementary modules allow practitioners to select operating points best suited to their evaluation constraints and resource availability.

5. Community Engagement and Open Source Ecosystem

The framework and codebase are fully open-sourced (https://github.com/EvolvingLMMs-Lab/lmms-eval) and are actively maintained with periodic task/model additions, bug fixes, and methodological upgrades. A public leaderboard is maintained (https://huggingface.co/spaces/lmms-lab/LiveBench) as a runtime companion to the toolkit. The design encourages:

• Direct community submission of new task datasets or model wrappers.
• Leaderboard submissions for model variants under blind evaluation.
• Modular integration with legacy and emerging multimodal benchmarks.

Future directions identified include algorithmic improvements for coreset construction, refined contamination analysis without training set access, and integration of further streaming/“live” benchmarking methods.

6. Impact and Limitations

LMMs-Eval provides an empirically validated, reproducible benchmarking ecosystem that can:

• Reveal performance gaps (e.g., open-source models vs. commercial APIs across tasks/domains).
• Accelerate model iteration by fostering fast, fair ablation studies.
• Enable auditing of evaluation contamination and rapid response to new use cases or emergent modalities.
• Support standardization of multimodal evaluation across the research community.

Limitations include the impossibility of perfect trilemma resolution, endurance of residual contamination risk when using static datasets, and dependence on the representativity of coreset selection for rapid evaluations. The continuous evolution of tasks, models, and threat models for contamination calls for ongoing methodological refinement and community vigilance (Zhang et al., 17 Jul 2024).

7. Summary Table: LMMs-Eval System Components

Component	Description	Role
Full Suite	All tasks, all models	Max coverage, increased cost and contamination
Lite Toolkit	Pruned, coreset-selected subset	Fast, correlated, low-cost evaluation
Multimodal LiveBench	Continuously updating, zero-contamination evaluation	Tests zero-shot generalization and recency
Leaderboard & API	Public result reporting, modular model/task support	Transparency, community benchmarking

LMMs-Eval thus constitutes a “reality check” for the evaluation landscape of large multimodal models. Its integrated approach—balancing holistic coverage, efficiency, and up-to-date evaluation—sets a standard for both the transparent benchmarking of LMM architectures and for steering future advances in multimodal AI methodology and model development (Zhang et al., 17 Jul 2024).