Summary of Visla Benchmark: Evaluating Embedding Sensitivity to Semantic and Lexical Alterations, by Sri Harsha Dumpala et al.
VISLA Benchmark: Evaluating Embedding 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: 25 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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 paper introduces the VISLA (Variance and Invariance to Semantic and Lexical Alterations) benchmark, a 3-way semantic (in)equivalence task designed to evaluate the semantic and lexical understanding of language models. The evaluation involves 34 vision-language models (VLMs) and 20 unimodal language models (ULMs), revealing difficulties in distinguishing between lexical and semantic variations. Spatial semantics encoded by VLMs are sensitive to lexical information, whereas text encoders demonstrate greater sensitivity to semantic and lexical variations than ULMs. The paper contributes to a deeper understanding of language models’ capabilities, highlighting strengths and weaknesses across diverse VLMs and ULMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how well computers can understand what we mean when we talk or write about things. It tests different computer models that can understand language, but these models have trouble telling the difference between words with similar meanings and actual real-world objects. The study shows that some models are better at understanding certain types of language than others. This helps us learn more about how computers think and what they’re good at. |
Keywords
» Artificial intelligence » Semantics
