FewMMBench: A Benchmark for Multimodal Few-Shot Learning
arXiv:2602.21854v1 Announce Type: new Abstract: As multimodal large language models (MLLMs) advance in handling interleaved image-text data, assessing their few-shot learning capabilities remains an open challenge. In this paper, we introduce FewMMBench, a comprehensive benchmark designed to evaluate MLLMs under few-shot conditions, with a focus on In-Context Learning (ICL) and Chain-of-Thought (CoT) prompting. Covering a diverse suite of multimodal understanding tasks, from attribute recognition to temporal reasoning, FewMMBench enables systematic analysis across task types, model families, and prompting strategies. We evaluate 26 open-weight MLLMs from six model families across zero-shot, few-shot, and CoT-augmented few-shot settings. Our findings reveal that instruction-tuned models exhibit strong zero-shot performance but benefit minimally, or even regress, with additional demonstrations or CoT reasoning. Retrieval-based demonstrations and increased context size also yield limited
arXiv:2602.21854v1 Announce Type: new Abstract: As multimodal large language models (MLLMs) advance in handling interleaved image-text data, assessing their few-shot learning capabilities remains an open challenge. In this paper, we introduce FewMMBench, a comprehensive benchmark designed to evaluate MLLMs under few-shot conditions, with a focus on In-Context Learning (ICL) and Chain-of-Thought (CoT) prompting. Covering a diverse suite of multimodal understanding tasks, from attribute recognition to temporal reasoning, FewMMBench enables systematic analysis across task types, model families, and prompting strategies. We evaluate 26 open-weight MLLMs from six model families across zero-shot, few-shot, and CoT-augmented few-shot settings. Our findings reveal that instruction-tuned models exhibit strong zero-shot performance but benefit minimally, or even regress, with additional demonstrations or CoT reasoning. Retrieval-based demonstrations and increased context size also yield limited gains. These results highlight FewMMBench as a rigorous testbed for diagnosing and advancing few-shot capabilities in multimodal LLMs. The data is available at: https://huggingface.co/datasets/mustafaa/FewMMBench
Executive Summary
The article introduces FewMMBench, a comprehensive benchmark for evaluating multimodal large language models' (MLLMs) few-shot learning capabilities. The benchmark assesses MLLMs under In-Context Learning (ICL) and Chain-of-Thought (CoT) prompting across various task types and model families. The authors evaluate 26 open-weight MLLMs, revealing that instruction-tuned models exhibit strong zero-shot performance but regress with additional demonstrations or CoT reasoning. The study highlights the limitations of current few-shot learning methods and presents FewMMBench as a rigorous testbed for advancing multimodal LLMs. The findings have implications for both practical applications and policy-making in the development of multimodal AI systems.
Key Points
- ▸ FewMMBench is a comprehensive benchmark for evaluating multimodal LLMs' few-shot learning capabilities.
- ▸ The benchmark assesses MLLMs under ICL and CoT prompting across various task types and model families.
- ▸ Instruction-tuned models exhibit strong zero-shot performance but regress with additional demonstrations or CoT reasoning.
Merits
Robust Evaluation Framework
FewMMBench provides a systematic and comprehensive evaluation framework for assessing multimodal LLMs' few-shot learning capabilities, enabling researchers to diagnose and advance their models.
Diverse Task Suite
The benchmark covers a diverse suite of multimodal understanding tasks, from attribute recognition to temporal reasoning, allowing for a thorough analysis of MLLMs' capabilities.
Open-Weight Model Evaluation
The study evaluates 26 open-weight MLLMs from six model families, providing a comprehensive understanding of the few-shot learning capabilities of various model architectures.
Demerits
Limited Generalizability
The study's findings may not be generalizable to all MLLMs, as the evaluation was conducted on a specific set of open-weight models.
Lack of Human Evaluation
The study relies solely on automated evaluation metrics, which may not capture the nuances of human evaluation and understanding.
Insufficient Contextual Understanding
The study focuses primarily on few-shot learning capabilities, but may not provide sufficient insights into the contextual understanding and reasoning abilities of MLLMs.
Expert Commentary
The article presents a significant contribution to the field of multimodal AI research, providing a comprehensive evaluation framework for assessing few-shot learning capabilities in MLLMs. The study's findings and FewMMBench benchmark have far-reaching implications for the development of more effective multimodal AI systems. However, the study's limitations, such as the lack of human evaluation and insufficient contextual understanding, highlight the need for future research to address these gaps. The article's results and recommendations underscore the importance of developing more transparent and explainable AI systems, ensuring their safe and responsible deployment.
Recommendations
- ✓ Develop more robust and transparent few-shot learning methods that can adapt to diverse task types and model families.
- ✓ Integrate human evaluation and contextual understanding into multimodal LLMs' evaluation frameworks to ensure more comprehensive assessment of their capabilities.
