Redundancy Principles for MLLMs Benchmarks (2501.13953v2)

Abstract: With the rapid iteration of Multi-modality LLMs (MLLMs) and the evolving demands of the field, the number of benchmarks produced annually has surged into the hundreds. The rapid growth has inevitably led to significant redundancy among benchmarks. Therefore, it is crucial to take a step back and critically assess the current state of redundancy and propose targeted principles for constructing effective MLLM benchmarks. In this paper, we focus on redundancy from three key perspectives: 1) Redundancy of benchmark capability dimensions, 2) Redundancy in the number of test questions, and 3) Cross-benchmark redundancy within specific domains. Through the comprehensive analysis over hundreds of MLLMs' performance across more than 20 benchmarks, we aim to quantitatively measure the level of redundancy lies in existing MLLM evaluations, provide valuable insights to guide the future development of MLLM benchmarks, and offer strategies to refine and address redundancy issues effectively. The code is available at https://github.com/zzc-1998/Benchmark-Redundancy.

• The paper introduces a framework to assess redundancy across evaluation dimensions in MLLM benchmarks.
• It empirically shows that over 50% of instances in many benchmarks are redundant, underlining evaluation inefficiencies.
• The study offers actionable guidelines to optimize benchmark design for more reliable and efficient model evaluations.

Redundancy Principles for MLLMs Benchmarks: An Analytical Overview

The paper "Redundancy Principles for MLLMs Benchmarks" offers a comprehensive examination of redundancy in Multi-modal LLMs (MLLMs) benchmarks, presenting a structured framework to address prevalent inefficiencies and overlaps. Recognizing the proliferation of evaluation benchmarks in the field, the authors deliberate on their redundancy from three primary perspectives: dimensional, instance, and cross-benchmark redundancy. The aim is to optimize the reliability and efficiency of model evaluations by proposing concrete strategies to mitigate redundancy.

Key Aspects and Methodologies

The authors underscore the critical role benchmarks play in the development and evaluation of MLLMs, highlighting the inefficiencies introduced by redundant evaluation metrics. The paper delineates three distinct redundancy types:

1. Dimensional Redundancy: When different tasks within a benchmark assess similar capabilities, leading to repetitious assessments.
2. Instance Redundancy: Occurs when specific instances within benchmarks are too similar to others, providing minimal additional insight.
3. Cross-Benchmark Redundancy: Arises when multiple benchmarks targeting similar domains overlap in their evaluation objectives, leading to duplicated efforts.

To empirically paper these types of redundancies, the authors introduce the Performance Correlation Redundancy Framework. This approach is centered on evaluating the correlation between MLLM performance rankings across different assessment criteria. High correlation indicates potential redundancy, suggesting that certain dimensions or instances do not contribute uniquely to the assessment of model capabilities.

Numerical Insights and Results

Through their systematic exploration using VLMEvalKit, the authors examine redundancy across over 20 benchmarks, applying robustness through diverse datasets and model evaluations. Analyzing instances, they found that for a majority of existing benchmarks, significant redundancy exists, with at least 50% of instances deemed redundant for effective model ranking. Dimensional analysis suggests that lower-performing MLLMs often exhibit higher redundancy across various benchmarks, while high-performing models show more variance.

Additionally, the paper of cross-benchmark redundancy in the mathematics domain reveals varying correlations. Specifically, MathVista's lower redundancy level and its diverse task inclusion point to either noise or unique domain elements, with further case studies required to draw precise conclusions.

Implications and Directions for Future Research

The insights gained from this analysis provide significant implications for future benchmark design and application. The framework and principles introduced encourage:

• Optimization of Benchmarks: By minimizing unnecessary overlap in tasks and instances, benchmarking processes become more streamlined and resource-efficient.
• Informed Benchmark Selection: For practitioners, choosing benchmarks with the highest cross-benchmark redundancy ensures comprehensive evaluation without excess resource expenditure.
• Balanced Benchmark Structuring: Ensuring benchmarks maintain independence in dimensions while appropriately reflecting domain representativeness.

The authors contend that integration of redundancy assessment into the development cycle of benchmarks is pivotal to enhancing their utility and accuracy. This paper lays the groundwork for more thoughtful and targeted use of benchmarks, potentially influencing how future MLLMs are trained and evaluated.

The ongoing evolution of AI and multi-modal learning necessitates ongoing research into how these benchmarks adapt and integrate new capabilities, with a call for more specialized benchmarks that focus on unique modeling aspects. The research not only provides a toolkit for evaluating current benchmarks but also envisions a future where benchmark design facilitates more meaningful advancements in AI capabilities.