Summary of Do Multimodal Language Models Really Understand Direction? a Benchmark For Compass Direction Reasoning, by Hang Yin et al.
Do Multimodal Language Models Really Understand Direction? A Benchmark for Compass Direction Reasoning
by Hang Yin, Zhifeng Lin, Xin Liu, Bin Sun, Kan Li
First submitted to arxiv on: 21 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 proposed Compass Direction Reasoning (CDR) benchmark is designed to evaluate multimodal language models’ (MLMs) capabilities in spatial and compass direction reasoning. The CDR benchmark includes three types of images that test MLMs’ understanding of up, down, left, right, north, south, east, and west directions. Our evaluation reveals that most MLMs struggle with direction reasoning, performing at random guessing levels. To improve MLM performance, we explore mixdata and CoT fine-tuning methods, which significantly enhance MLM performance in compass direction reasoning by incorporating diverse data and step-by-step reasoning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new benchmark to test how well language models understand directions like up, down, left, right, north, south, east, and west. The benchmark has three types of images that challenge the models’ ability to recognize these directions. Surprisingly, most language models did poorly on this task, guessing randomly. To improve their performance, the authors tried different ways of training the models, like using a mix of different data and fine-tuning them step-by-step. |
Keywords
» Artificial intelligence » Fine tuning
