Summary of Data Efficient Evaluation Of Large Language Models and Text-to-image Models Via Adaptive Sampling, by Cong Xu et al.
Data Efficient Evaluation of Large Language Models and Text-to-Image Models via Adaptive Sampling
by Cong Xu, Gayathri Saranathan, Mahammad Parwez Alam, Arpit Shah, James Lim, Soon Yee Wong, Foltin Martin, Suparna Bhattacharya
First submitted to arxiv on: 21 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL)
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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 paper introduces SubLIME, an efficient evaluation framework for large language models (LLMs) and text-to-image models. The framework uses adaptive sampling techniques, such as clustering and quality-based methods, to create representative subsets of benchmarks. This approach ensures statistically aligned model rankings compared to full datasets, with high Pearson correlation coefficients (0.85-0.95). The authors analyze six NLP benchmarks and find that different sampling methods excel in specific tasks, while a single method does not universally outperform others. The framework is extended to cover 25 text-to-image models on 17 benchmarks, using a dynamic selection of optimal techniques for each benchmark. SubLIME reduces evaluation costs while preserving ranking integrity and score distribution. Additionally, the paper explores difficulty-based sampling to target challenging benchmark segments, enhancing model differentiation. The authors also identify redundancy across benchmarks within specific LLM categories. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us better understand big language models and text-to-image models by making it easier to test them. Usually, testing these models takes a lot of computing power, but this new approach uses special techniques to create smaller groups of tests that still give accurate results. The researchers tested six different kinds of language model tasks and found that different approaches work best for each task. They also showed how their method can be used with many other types of models and benchmarks. Overall, this paper helps us understand these powerful models better and makes it easier to compare them. |
Keywords
* Artificial intelligence * Clustering * Language model * Nlp
