Summary of A Comprehensive Survey Of Small Language Models in the Era Of Large Language Models: Techniques, Enhancements, Applications, Collaboration with Llms, and Trustworthiness, by Fali Wang et al.
A Comprehensive Survey of Small Language Models in the Era of Large Language Models: Techniques, Enhancements, Applications, Collaboration with LLMs, and Trustworthiness
by Fali Wang, Zhiwei Zhang, Xianren Zhang, Zongyu Wu, Tzuhao Mo, Qiuhao Lu, Wanjing Wang, Rui Li, Junjie Xu, Xianfeng Tang, Qi He, Yao Ma, Ming Huang, Suhang Wang
First submitted to arxiv on: 4 Nov 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 explores the limitations of Large Language Models (LLMs) in various tasks and domains, particularly their large parameter sizes and computational demands. While LLMs like PaLM 540B and Llama-3.1 405B excel in text generation, question answering, and reasoning, they require cloud API use, raise privacy concerns, and increase fine-tuning costs. The paper highlights the need for Small Language Models (SLMs) that are more cost-effective, efficient, and adaptable to resource-limited environments. SLMs are particularly well-suited for applications requiring localized data handling, minimal inference latency, and domain knowledge acquisition through lightweight fine-tuning. Despite their growing demand, a comprehensive survey on the definition, acquisition, application, enhancement, and reliability of SLMs is lacking. This paper proposes a standardized definition of SLMs based on their capability to perform specialized tasks and suitability for resource-constrained settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper talks about how big language models are good at doing things like generating text and answering questions, but they have some problems. They use too much computer power and memory, which can be a problem if you want to use them on devices that don’t have a lot of power or memory. They also need special equipment to work properly, which can make it hard to keep your personal information private. The paper says that there are smaller language models that could be used instead, but they haven’t been studied very much yet. |
Keywords
» Artificial intelligence » Fine tuning » Inference » Llama » Palm » Question answering » Text generation
