Summary of Sugarcrepe++ Dataset: Vision-language Model Sensitivity to Semantic and Lexical Alterations, by Sri Harsha Dumpala et al.
SUGARCREPE++ Dataset: Vision-Language Model Sensitivity to Semantic and Lexical Alterations
by Sri Harsha Dumpala, Aman Jaiswal, Chandramouli Sastry, Evangelos Milios, Sageev Oore, Hassan Sajjad
First submitted to arxiv on: 17 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Computation and Language (cs.CL); Machine Learning (cs.LG)
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 As machine learning educators writing for a technical audience, we summarize this paper as follows: This research introduces the SUGARCREPE++ dataset to analyze the sensitivity of vision-and-language models (VLMs) and unimodal language models (ULMs) to lexical and semantic alterations. The dataset consists of images paired with triplets of captions, including semantically equivalent but lexically different positive captions and one hard negative caption. This poses a 3-way semantic (in)equivalence problem for the models. The study evaluates VLMs and ULMs that differ in architecture, pre-training objectives, and datasets to benchmark their performance on SUGARCREPE++. Experimental results show that VLMs struggle to distinguish between lexical and semantic variations, particularly in object attributes and spatial relations. While larger models with more extensive pre-training achieve better performance, there is still a significant opportunity for improvement. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research focuses on improving the understanding of language models by analyzing their ability to comprehend precise semantics. The study introduces a new dataset called SUGARCREPE++ that tests the models’ ability to distinguish between semantically equivalent but lexically different captions. This is an important challenge because it can help us better understand how language models work and how we can improve them. |
Keywords
* Artificial intelligence * Machine learning * Semantics
