Summary of Boosting the Capabilities Of Compact Models in Low-data Contexts with Large Language Models and Retrieval-augmented Generation, by Bhargav Shandilya and Alexis Palmer
Boosting the Capabilities of Compact Models in Low-Data Contexts with Large Language Models and Retrieval-Augmented Generation
by Bhargav Shandilya, Alexis Palmer
First submitted to arxiv on: 1 Oct 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 Retrieval Augmented Generation (RAG) framework utilizes a large language model (LLM) to correct the output of a smaller model for morphological glossing tasks in low-resource languages. By leveraging linguistic knowledge, this approach can bridge the data scarcity gap by providing models with useful inductive bias. The RAG framework is backed by an LLM and aims to make up for lack of data and trainable parameters. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper presents a new way to help language models understand morphological glossing tasks better, especially when there’s not much training data available. By using linguistic rules and a large language model, the RAG framework can improve the performance of smaller models and make it easier to process languages with limited resources. |
Keywords
» Artificial intelligence » Large language model » Rag » Retrieval augmented generation
