Redundant benchmarks inflate Multi-modal Large Language Model scores;
MLLM benchmarks are echo chambers; this paper provides principles to break the repetition.
It identifies and analyzes redundancy in Multi-modal Large Language Model benchmarks. It proposes principles to measure and mitigate redundancy for more reliable evaluations.
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https://arxiv.org/abs/2501.13953
Original Problem: 🧐:
→ Multi-modal Large Language Model benchmarks suffer from significant redundancy.
→ This redundancy exists in questions, answers and visual content.
→ Redundancy inflates performance metrics and makes evaluations unreliable.
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Solution in this Paper: 💡:
→ This paper introduces "redundancy principles" to analyze Multi-modal Large Language Model benchmarks.
→ It identifies three types of redundancy: question, answer, and visual redundancy.
→ Question redundancy refers to paraphrased questions testing the same knowledge.
→ Answer redundancy means similar answers are considered correct for different questions.
→ Visual redundancy involves similar or repetitive visual inputs across the benchmark.
→ The paper uses metrics like question similarity, answer similarity, and visual feature similarity to quantify redundancy.
→ They propose methods to mitigate redundancy through dataset curation and adversarial filtering techniques.
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Key Insights from this Paper: 🤔:
→ Redundancy in benchmarks leads to overestimation of Multi-modal Large Language Model performance.
→ High redundancy makes benchmarks less sensitive to actual model improvements.
→ Analyzing and reducing redundancy is crucial for creating robust and reliable benchmarks.
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Results: 📊:
→ Redundancy analysis reveals significant overlap in existing Multi-modal Large Language Model benchmarks questions and answers.
→ Mitigating redundancy provides a more realistic evaluation of Multi-modal Large Language Models.
→ Redundancy-reduced benchmarks are more effective in differentiating model performance and progress.
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