MMLONGBENCH: A New Benchmark for Long-Context Vision-Language Models
Understanding Long-Context Vision-Language Models
Recent advancements in long-context modeling have greatly improved the performance of large language models (LLMs) and large vision-language models (LVLMs). These long-context vision-language models (LCVLMs) can now process extensive amounts of data, including hundreds of images and thousands of text tokens, in a single operation. However, the lack of effective evaluation benchmarks has created uncertainty about their performance in real-world applications.
Challenges with Existing Benchmarks
Current benchmarks for evaluating these models have several significant limitations:
- Narrow Task Coverage: They do not encompass a wide range of downstream tasks.
- Image Type Limitations: They fail to include diverse image types.
- Context Length Control: There is a lack of control over context lengths.
- Single Length Evaluations: They typically evaluate models at only one context length.
To address these issues, various techniques have been developed to extend context windows for LVLMs, such as longer pre-training lengths and efficient architectures. Notable models like Gemini-2.5 and Qwen2.5-VL have successfully implemented these methods.
Introducing MMLONGBENCH
A collaborative team from institutions such as HKUST and NVIDIA has introduced MMLONGBENCH, the first comprehensive benchmark for LCVLMs. This benchmark includes:
- 13,331 examples across five downstream task categories.
- Coverage of both natural and synthetic image types.
- Standardized input lengths ranging from 8K to 128K tokens.
The evaluation process involved testing 46 different models, revealing that performance in single tasks does not reliably predict overall long-context capabilities. While closed-source models generally performed better, all models faced challenges with long-context tasks.
Methodology and Evaluation Process
To create long-context scenarios, researchers used gold passages containing answers mixed with distracting passages from Wikipedia. This method allowed for the evaluation of various tasks, including image classification across multiple datasets. The results showed that all models struggled with long-context vision-language tasks, with the top performer, Gemini-2.5-Pro, achieving a notable score.
Key Findings
Some of the key findings from the MMLONGBENCH evaluation include:
- Models generally performed poorly on long-context tasks, with GPT-4o achieving an average score of 62.9.
- Gemini-2.5-Pro outperformed other models by 20 points in most tasks.
- Models demonstrated some ability to generalize beyond their training context lengths.
Conclusion
The introduction of MMLONGBENCH represents a significant step forward in evaluating LCVLMs. This benchmark provides a robust framework for assessing model capabilities across various tasks and context lengths. The findings highlight the need for improved evaluation methods and underscore the challenges faced by current models in handling long-context scenarios. MMLONGBENCH sets a new standard for future research, guiding the development of more efficient and capable vision-language models.
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