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Models/MC-CoT F-Large

MC-CoT F-Large

Reported on 9 benchmarks across 1 task · 1 paper · 8 SOTA

Note: results are matched by exact model name. Different papers may use the same name for different model variants.

Natural Language Processing9 results

  • Question AnsweringonScienceQA
    Avg. Accuracy· 2023-11-23
    94.88
    SOTA
    Boosting the Power of Small Multimodal Reasoning Models to Match Larger Models with Self-Consistency TrainingarXiv:2311.14109
  • Question AnsweringonScienceQA
    Grades 1-6· 2023-11-23
    95.3
    SOTA
    Boosting the Power of Small Multimodal Reasoning Models to Match Larger Models with Self-Consistency TrainingarXiv:2311.14109
  • Question AnsweringonScienceQA
    Grades 7-12· 2023-11-23
    94.13
    SOTA
    Boosting the Power of Small Multimodal Reasoning Models to Match Larger Models with Self-Consistency TrainingarXiv:2311.14109
  • Question AnsweringonScienceQA
    Image Context· 2023-11-23
    93.75
    SOTA
    Boosting the Power of Small Multimodal Reasoning Models to Match Larger Models with Self-Consistency TrainingarXiv:2311.14109
  • Question AnsweringonScienceQA
    Language Science· 2023-11-23
    93.18
    SOTA
    Boosting the Power of Small Multimodal Reasoning Models to Match Larger Models with Self-Consistency TrainingarXiv:2311.14109
  • Question AnsweringonScienceQA
    Natural Science· 2023-11-23
    97.47
    SOTA
    Boosting the Power of Small Multimodal Reasoning Models to Match Larger Models with Self-Consistency TrainingarXiv:2311.14109
  • Question AnsweringonScienceQA
    No Context· 2023-11-23
    94.49
    SOTA
    Boosting the Power of Small Multimodal Reasoning Models to Match Larger Models with Self-Consistency TrainingarXiv:2311.14109
  • Question AnsweringonScienceQA
    Text Context· 2023-11-23
    96.97
    SOTA
    Boosting the Power of Small Multimodal Reasoning Models to Match Larger Models with Self-Consistency TrainingarXiv:2311.14109
  • Question AnsweringonScienceQA
    Social Science· 2023-11-23
    90.44
    best: 96.29 (Honeybee)
    Boosting the Power of Small Multimodal Reasoning Models to Match Larger Models with Self-Consistency TrainingarXiv:2311.14109