Summary of Mm-poe: Multiple Choice Reasoning Via. Process Of Elimination Using Multi-modal Models, by Sayak Chakrabarty et al.
MM-PoE: Multiple Choice Reasoning via. Process of Elimination using Multi-Modal Models
by Sayak Chakrabarty, Souradip Pal
First submitted to arxiv on: 10 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces a novel methodology, Multiple Choice Reasoning via Process of Elimination using Multi-Modal models (MM-PoE), designed to improve the efficacy of Vision-Language Models (VLMs) in multiple-choice visual reasoning tasks. Unlike conventional approaches, MM-PoE employs a dual-step scoring paradigm that initially identifies and excludes implausible choices, then focuses on the most probable remaining options. This method mimics human test-taking strategies by eliminating clearly incorrect answers before selecting the optimal response. The authors evaluate their approach across three benchmark datasets, demonstrating significant improvements in both zero-shot and few-shot performance of contemporary state-of-the-art VLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps computers better understand pictures and answer questions about what’s happening in them. It creates a new way for machines to think through multiple-choice problems by eliminating obviously wrong answers first. This makes the machines more accurate when answering visual questions, especially when they haven’t been trained on those specific types of questions before. |
Keywords
» Artificial intelligence » Few shot » Multi modal » Zero shot
