Summary of Vygotsky Distance: Measure For Benchmark Task Similarity, by Maxim K. Surkov and Ivan P. Yamshchikov
Vygotsky Distance: Measure for Benchmark Task Similarity
by Maxim K. Surkov, Ivan P. Yamshchikov
First submitted to arxiv on: 22 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 presents a novel approach to evaluating natural language processing (NLP) benchmarks by introducing the “Vygotsky distance” similarity measure. The Vygotsky distance is calculated based on the relative performance of models on different tasks, rather than the properties of the tasks themselves. This allows for significant reductions in the number of evaluation tasks while maintaining high validation quality. Experiments on various benchmarks, including GLUE, SuperGLUE, CLUE, and RussianSuperGLUE, demonstrate that a vast majority of NLP benchmarks could be reduced by at least 40%. The Vygotsky distance can also be used for validating new tasks, increasing the generalization potential of future NLP models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about finding a better way to test language processing machines. Right now, we use many different tests that don’t help us understand how well these machines will work in real-life situations. The authors came up with a new idea called “Vygotsky distance” that helps us figure out which tests are most important and which ones we can skip. They tested this idea on many different sets of language processing tasks and found that we could get rid of almost 40% of the tests without losing any information. This is exciting because it will make it easier for scientists to test these machines and make them better. |
Keywords
* Artificial intelligence * Generalization * Natural language processing * Nlp
