Benchmark Everything Everywhere All at Once

Problem
The paper addresses the limitations of existing benchmark construction methods for large language models (LLMs) and multimodal language models (MLLMs), which are often labor-intensive, difficult to reuse, and prone to performance saturation. This work is particularly relevant as it is a preprint and unreviewed, highlighting the need for scalable and sustainable benchmarking solutions in the rapidly evolving AI landscape.

Method
The authors introduce Benchmark Agent, an autonomous framework that automates the entire benchmark construction pipeline. This includes user query analysis, subtask design, data annotation, and quality control. The system is designed to minimize human involvement while ensuring high-quality outputs. The authors implemented Benchmark Agent to generate 15 diverse benchmarks that cover various evaluation scenarios, such as text understanding, multimodal understanding, and domain-specific reasoning. The architecture and specific algorithms used in Benchmark Agent are not detailed in the abstract, but the emphasis is on its autonomous capabilities and the systematic approach to benchmark generation.

Results
Benchmark Agent was evaluated through extensive experiments, including human evaluations and assessments using LLMs as judges. The results indicate that the benchmarks produced are of high quality, with minimal human intervention required. Notably, the authors found that current state-of-the-art models exhibit significant difficulties in certain domain-specific reasoning tasks, suggesting areas for further research and improvement. The paper does not provide specific quantitative metrics or comparisons against named baselines, which would strengthen the results section.

Limitations
The authors acknowledge that while Benchmark Agent can generate high-quality benchmarks, the reliance on automated processes may introduce biases or overlook nuanced aspects of benchmark quality that human evaluators might catch. Additionally, the benchmarks produced may still be subject to the limitations of the underlying models used for evaluation. The lack of detailed performance metrics and comparisons with existing benchmarks is another limitation that could be addressed in future work.

Why it matters
The introduction of Benchmark Agent has significant implications for the AI research community, as it offers a scalable and efficient method for benchmark creation, potentially accelerating the evaluation and advancement of LLMs and MLLMs. The ability to rapidly evolve benchmarks can help maintain the relevance and discriminative power of evaluation metrics in a fast-paced field. This work contributes to the ongoing discourse on sustainable AI practices and the need for robust evaluation frameworks, as discussed in related literature (see arXiv cs.AI). The authors plan to make the preview and code publicly available, further promoting collaboration and innovation in benchmark development.