Summary of Aloe: a Family Of Fine-tuned Open Healthcare Llms, by Ashwin Kumar Gururajan et al.
Aloe: A Family of Fine-tuned Open Healthcare LLMs
by Ashwin Kumar Gururajan, Enrique Lopez-Cuena, Jordi Bayarri-Planas, Adrian Tormos, Daniel Hinjos, Pablo Bernabeu-Perez, Anna Arias-Duart, Pablo Agustin Martin-Torres, Lucia Urcelay-Ganzabal, Marta Gonzalez-Mallo, Sergio Alvarez-Napagao, Eduard Ayguadé-Parra, Ulises Cortés Dario Garcia-Gasulla
First submitted to arxiv on: 3 May 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 As the capabilities of Large Language Models (LLMs) continue to advance in healthcare and medicine, there is a growing need for competitive open-source models that safeguard public interest. This work explores methods to improve current open models, including instruct tuning, model merging, alignment, red teaming, and advanced inference schemes. The Aloe family, a set of open medical LLMs, are highly competitive within their scale range. Trained on the best base models (Mistral, LLaMA 3) using a custom dataset combining public data sources improved with synthetic Chain of Thought (CoT), Aloe models undergo alignment to become policy-aligned open healthcare LLMs. Evaluation includes bias and toxicity datasets, red teaming efforts, and risk assessments for healthcare LLMs. The study also explores advanced prompt engineering strategies to boost performance across benchmarks, yielding state-of-the-art results for open healthcare 7B LLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making better language models that can help people in the medical field. The researchers want to make sure these models are fair and don’t have any biases or harm anyone. They did this by training their models on a special dataset and making them work together with other models. They also tested the models to see if they were good or not, using things like fake data to test how well they worked. The results show that their models are really good at helping people in medicine and can make important decisions. |
Keywords
» Artificial intelligence » Alignment » Inference » Llama » Prompt
