Lightchen233

M 3 CoT Home Download Evaluation Annotation Paper Code Citation Contact Leaderboard Explore Visualize Leaderboard - M 3 CoT Setting Selection All Zero-shot Tool-Usage Fine-Tuning Preference-Optimization Evaluation of different methods on the test split (whole: 2,359). The accuracies across various categories and the overall average are reported below. 😀 You are invited to contribute your results to the M 3 CoT test split! Please send your result scores to this email or open a new issue at the github repository . ⚠️⚠️⚠️ Caveat: The data in the leaderboard is collected manually from existing papers. There might be some errors in the data, ambiguous data due to different interpretations, and missing data due to the lack of information in the papers. Make sure to double-check the data before using it. Please contact us at this email if you find any errors or have any suggestions. We appreciate your contributions and feedback. # Model Prompt Method --> Learning --> #Setting #Size #Backbone #P --> Link Date --> Lang Natural Social Physical SocialCS Temporal Algebra Geometry Theory G1-6 --> G7-12 --> Total Prompting strategies: Direct : approach to submitting samples in the VLLMs required format CoT : with ''Let's think step-by-step!'' Desp-CoT : with an initial image description prompting CCoT : with better description in graph format Setting: Zero-shot : The model is evaluated in a zero-shot setting on M 3 CoT Tool-Usage : The model is evaluated in a tool-augmented setting on M 3 CoT Fine-tuning : The model is fine-tuned on M 3 CoT - : Not available #Size: Total number of parameters in the model Accuracies for different question sets: NAT : questions of the natural science subject --> Lang : questions of the language science subject Natural : questions of the natural science subject Social : questions of the social science subject Physical : questions of the physical commonsense SocialCS : questions of the social commonsense Temporal : questions of the temporal...