Summary of Are Small Language Models Ready to Compete with Large Language Models For Practical Applications?, by Neelabh Sinha et al.
Are Small Language Models Ready to Compete with Large Language Models for Practical Applications?
by Neelabh Sinha, Vinija Jain, Aman Chadha
First submitted to arxiv on: 17 Jun 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 proposes a framework for evaluating small, open Language Models (LMs) in practical settings by measuring their semantic correctness across three aspects: task types, application domains, and reasoning types. The framework aims to bridge the gap between smaller LMs’ limitations and their potential applications. By conducting an in-depth comparison of 10 small, open LMs using the proposed framework, the authors identify the best LM and prompt style depending on specific application requirements. Notably, they show that well-selected small LMs can outperform state-of-the-art (SOTA) LLMs like DeepSeek-v2, GPT-4o, and GPT-4o-mini. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us choose the best small Language Model for a specific job by testing how well it does in different situations. It compares 10 smaller models to see which one works best with certain types of tasks or applications. The authors even show that some of these smaller models can do better than more powerful models if used correctly. |
Keywords
» Artificial intelligence » Gpt » Language model » Prompt
