Summary of Benchmark Transparency: Measuring the Impact Of Data on Evaluation, by Venelin Kovatchev and Matthew Lease
Benchmark Transparency: Measuring the Impact of Data on Evaluation
by Venelin Kovatchev, Matthew Lease
First submitted to arxiv on: 31 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 The proposed paper explores the relationship between data distribution and NLP model performance, introducing a novel automated framework to quantify this impact. The framework assesses six key dimensions of data point distribution: ambiguity, difficulty, discriminability, length, noise, and perplexity. This study aims to shed light on the complex interplay between these factors and their effects on model evaluation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The research investigates how different aspects of data distribution affect NLP models’ performance and evaluation. By analyzing six key dimensions (ambiguity, difficulty, discriminability, length, noise, and perplexity), the study aims to better understand the impact of data distribution on model performance. This knowledge can help improve the accuracy and reliability of NLP models. |
Keywords
» Artificial intelligence » Nlp » Perplexity
