Summary of Zero-shot Commonsense Reasoning Over Machine Imagination, by Hyuntae Park et al.
Zero-shot Commonsense Reasoning over Machine Imagination
by Hyuntae Park, Yeachan Kim, Jun-Hyung Park, SangKeun Lee
First submitted to arxiv on: 12 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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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 This paper proposes a novel zero-shot commonsense reasoning framework called Imagine, which complements textual inputs with visual signals derived from machine-generated images. The authors aim to bridge the gap between Pre-trained Language Models (PLMs) and humans by incorporating imagination capabilities into PLMs. To achieve this, they enhance PLMs with an image generator and create a synthetic pre-training dataset that simulates visual question-answering. The framework outperforms existing methods on diverse reasoning benchmarks, highlighting the strength of machine imagination in mitigating reporting bias and enhancing generalization capabilities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine is a new way for language models to reason about the world without being trained on specific situations. It uses images to help the model understand what’s happening, which makes it better at answering questions that humans can answer too. The authors created a special dataset with fake image-based questions and answers to train the model. When they tested it, Imagine did much better than other methods, showing that using images helps language models be more like humans. |
Keywords
» Artificial intelligence » Generalization » Question answering » Zero shot
