Summary of Rag Vs Fine-tuning: Pipelines, Tradeoffs, and a Case Study on Agriculture, by Angels Balaguer et al.
RAG vs Fine-tuning: Pipelines, Tradeoffs, and a Case Study on Agriculture
by Angels Balaguer, Vinamra Benara, Renato Luiz de Freitas Cunha, Roberto de M. Estevão Filho, Todd Hendry, Daniel Holstein, Jennifer Marsman, Nick Mecklenburg, Sara Malvar, Leonardo O. Nunes, Rafael Padilha, Morris Sharp, Bruno Silva, Swati Sharma, Vijay Aski, Ranveer Chandra
First submitted to arxiv on: 16 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: 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 The proposed pipeline combines fine-tuning and Retrieval-Augmented Generation (RAG) methods for Large Language Models (LLMs), evaluating their trade-offs on popular models like Llama2-13B, GPT-3.5, and GPT-4. The pipeline includes stages such as extracting PDF information, generating questions and answers, fine-tuning, and leveraging GPT-4 for evaluation. Metrics are proposed to assess the performance of each stage. An agricultural dataset is used to study a potentially disruptive application: providing location-specific insights to farmers. Results show the effectiveness of the pipeline in capturing geographic-specific knowledge and demonstrate the cumulative benefits of fine-tuning (6% accuracy increase) and RAG (5% accuracy increase). The fine-tuned model also leverages information across geographies, increasing answer similarity from 47% to 72%. This showcases LLMs’ adaptability for industrial domains. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models are getting better at understanding us. But what if we want them to understand specific industries or locations? In this paper, researchers propose a new way to make language models more useful. They combine two methods: fine-tuning and Retrieval-Augmented Generation (RAG). Fine-tuning helps the model learn from specific data, while RAG adds extra information to help it understand certain topics better. The researchers tested these methods on different types of models and found that they work well together. They also showed how this technology could be used in agriculture, giving farmers location-specific insights that can help them make better decisions. |
Keywords
* Artificial intelligence * Fine tuning * Gpt * Rag * Retrieval augmented generation
