Summary of Benchmarking Foundation Models on Exceptional Cases: Dataset Creation and Validation, by Suho Kang et al.
Benchmarking Foundation Models on Exceptional Cases: Dataset Creation and Validation
by Suho Kang, Jungyang Park, Joonseo Ha, SoMin Kim, JinHyeong Kim, Subeen Park, Kyungwoo Song
First submitted to arxiv on: 23 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 investigates the performance of foundation models (FMs) in exceptional scenarios, defined as out-of-distribution (OOD) reasoning tasks. To address this gap, the authors develop a novel dataset comprising graphic novels, calligraphy, news articles, and lyrics across multiple modalities. The dataset includes instance classification, character recognition, token prediction, and text generation tasks. Additionally, the paper proposes prompt engineering techniques like Chain-of-Thought (CoT) and CoT+Few-Shot to enhance FM performance. Experimental results validate the effectiveness of these methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research focuses on how well AI models perform when given unusual or unexpected information. The authors created a special dataset with different types of text, such as comics and news articles, to test how well these AI models can understand and respond to new situations. They also developed new techniques to help the models work better in these exceptional scenarios. The results show that their methods improve the performance of these AI models. |
Keywords
» Artificial intelligence » Classification » Few shot » Prompt » Text generation » Token
