Summary of Cost-effective, High-performance Open-source Llms Via Optimized Context Retrieval, by Jordi Bayarri-planas and Ashwin Kumar Gururajan and Dario Garcia-gasulla
Cost-Effective, High-Performance Open-Source LLMs via Optimized Context Retrieval
by Jordi Bayarri-Planas, Ashwin Kumar Gururajan, Dario Garcia-Gasulla
First submitted to arxiv on: 23 Sep 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 presents a study that optimizes the use of Large Language Models (LLMs) in healthcare, addressing concerns over factual accuracy and high costs by using open-source models. The researchers demonstrate that optimized context retrieval can achieve high performance at a lower cost than proprietary models, outperforming them on medical question answering tasks. They also introduce OpenMedQA, a new benchmark for open-ended medical question answering that addresses limitations of multiple-choice formats. Additionally, the study provides practical guidelines for implementing optimized context retrieval and empirical validation of its cost-effectiveness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In simple terms, this research makes healthcare AI more accessible and affordable by using free models instead of expensive ones. The scientists found a way to make these models work better by optimizing how they retrieve information. They also created a new test to evaluate the quality of medical question answering systems that can ask open-ended questions. This helps ensure that healthcare AI solutions are accurate, effective, and beneficial for patients. |
Keywords
» Artificial intelligence » Question answering
