Summary of Babelbench: An Omni Benchmark For Code-driven Analysis Of Multimodal and Multistructured Data, by Xuwu Wang et al.
BabelBench: An Omni Benchmark for Code-Driven Analysis of Multimodal and Multistructured Data
by Xuwu Wang, Qiwen Cui, Yunzhe Tao, Yiran Wang, Ziwei Chai, Xiaotian Han, Boyi Liu, Jianbo Yuan, Jing Su, Guoyin Wang, Tingkai Liu, Liyu Chen, Tianyi Liu, Tao Sun, Yufeng Zhang, Sirui Zheng, Quanzeng You, Yang Yang, Hongxia Yang
First submitted to arxiv on: 1 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 addresses a crucial gap in the evaluation of large language models (LLMs) by introducing BabelBench, a unified benchmark framework that assesses their proficiency in managing complex data types. The proposed framework evaluates LLMs’ abilities in multimodal multistructured data processing, structured data processing, and code generation. The dataset consists of 247 carefully curated problems that challenge the models with tasks such as perception, commonsense reasoning, logical reasoning, and more. The results demonstrate that even state-of-the-art models like ChatGPT-4 have significant room for improvement. This research offers valuable insights and guidance for future studies in the field. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how well large language models can work with different types of data. Right now, there’s no single way to test these models’ abilities, so researchers are using different methods that don’t really compare. To fix this, the authors created a new tool called BabelBench, which has 247 problems that challenge the models in various ways. They found out that even the best models have a lot to learn, and this research can help others make better models. |
