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4 | Oryx Video-ChatGPT 5 |

6 | 7 | #### [Sara Pieri](https://scholar.google.com/citations?user=jLNKLsgAAAAJ&hl=en&oi=ao)\*, [Sahal Shaji Mullappilly](https://scholar.google.com/citations?user=LJWxVpUAAAAJ&hl=en)*, [Fahad Khan](https://sites.google.com/view/fahadkhans/home), [Rao Muhammad Anwer](https://scholar.google.com/citations?hl=en&authuser=1&user=_KlvMVoAAAAJ), [Salman Khan](https://salman-h-khan.github.io/), [Timothy Baldwin](https://scholar.google.com/citations?user=wjBD1dkAAAAJ&hl=en), and [Hisham Cholakkal](https://scholar.google.com/citations?hl=en&user=bZ3YBRcAAAAJ) 8 | \* Equally contributing first authors 9 | 10 | #### **Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI), UAE** 11 | 12 | [![Website](https://img.shields.io/badge/Project-Website-87CEEB)](https://github.com/mbzuai-oryx/BiMediX) 13 | [![HuggingFace](https://img.shields.io/badge/HuggingFace-Page-F9D371)](https://huggingface.co/BiMediX) 14 | [![Paper](https://img.shields.io/badge/arXiv-Paper-.svg)](https://arxiv.org/abs/2402.13253) 15 | [![Demo](https://img.shields.io/badge/Gradio-Demo-red)](https://0ae6-16-1-32-178.ngrok-free.app/) 16 | [![License](https://img.shields.io/badge/License-CC%20BY--NC--SA%204.0-lightgrey)](https://github.com/mbzuai-oryx/BiMediX/blob/main/LICENSE.txt) 17 | 18 | [![Video Title](images/tumbnail.png)](https://youtu.be/kqfEdAcazIg) 19 | 20 | --- 21 | 22 | ## 📢 Latest Updates 23 | - **Sep-20-24**: Accepted to Findings of EMNLP 2024. 24 | - **Feb-22-24**: Trained [models](https://huggingface.co/BiMediX) and [demo](https://0ae6-16-1-32-178.ngrok-free.app/) are live. 🔥🔥 25 | - **Feb-21-24**: BiMediX paper is released [arxiv link](https://arxiv.org/abs/2402.13253). 🔥🔥 26 | - 📦 Code and datasets coming soon! 🚀 27 | --- 28 | 29 | ## :woman_health_worker: Overview 30 | We introduce BiMediX, the first bilingual medical mixture of experts LLM designed for seamless interaction in both English and Arabic. 31 | Our model facilitates a wide range of **medical interactions** in English and Arabic, including multi-turn chats to inquire about additional details such as patient symptoms and medical history, multiple-choice question answering, and open-ended question answering. 32 | 33 | Our models are available for download at the Project's [HuggingFace Page](https://huggingface.co/BiMediX). 34 | 35 | --- 36 | 37 | ## 🏆 Contributions 38 | 39 | Our contributions are as follows: 40 | 41 | - We introduce BiMediX, the first bilingual medical LLM with expertise in both English and Arabic, enabling seamless medical interactions such as multi-turn chats, multiple choice, and closed question answering. 42 | - We developed a semi-automated translation pipeline with human verification for high-quality translation of English medical texts into Arabic, aiding in the creation of a dataset and benchmark for Arabic healthcare LLM evaluation. 43 | - We curated the BiMed1.3M dataset, a comprehensive Arabic-English bilingual instruction set with over 1.3 million instructions and 632 million healthcare-specialized tokens, supporting diverse medical interactions and enabling a chatbot for patient follow-ups, with a focus on a 1:2 Arabic-to-English ratio across medical content. 44 | - **BiMediX outperforms existing models in medical benchmarks while being 8-times faster than comparable existing approaches.** 45 | 46 | 47 | --- 48 | 49 | ## ⚡ Model 50 | 51 | - The BiMediX model, built on the state-of-the-art Mixture of Experts (MoE) architecture, leverages the [Mixtral-8x7B](https://huggingface.co/mistralai/Mixtral-8x7B-v0.1) base model. This approach enables the model to scale significantly by utilizing a sparse operation method, where less than 13 billion parameters are active during inference, enhancing efficiency. 52 | - The training utilized the **BiMed1.3M** dataset, focusing on bilingual medical interactions in both English and Arabic, with a substantial corpus of over 632 million healthcare-specialized tokens. 53 | - The fine-tuning process included **QLoRA**, of both experts and router, to adapt the model efficiently to specific tasks while keeping computational demands manageable. 54 | 55 | 56 | 57 | | Model Name | Link Download | 58 | |-----------------------------------------------------|----------------------------------------------------------------------| 59 | | BiMediX-Bilingual | [HuggingFace](https://huggingface.co/BiMediX/BiMediX-Bi) | 60 | | BiMediX-Arabic | [HuggingFace](https://huggingface.co/BiMediX/BiMediX-Ara) | 61 | | BiMediX-English | [HuggingFace](https://huggingface.co/BiMediX/BiMediX-Eng) | 62 | 63 | 64 | 65 | 66 | 67 | --- 68 | 69 | ## 🔍 Data 70 | 71 | The BiMed1.3M dataset, central to BiMediX's training, was meticulously compiled to include a wide range of medical interactions. The creation process involved generating multi-turn chat conversations using ChatGPT, based on publicly available medical MCQAs to simulate realistic doctor-patient dialogues. This dataset includes over 200,000 high-quality **multi-turn medical dialogues**, enriching the model's training material. 72 | 73 | 74 |

75 | data gif 76 |

77 | 78 | A semi-automated, iterative translation process was employed to create high-quality Arabic versions of the data, utilizing ChatGPT for initial translations and human professionals for refinement. This ensured the dataset's fidelity and relevance across both English and Arabic. 79 | Furthermore, we translated the English evaluation set to Arabic to evaluate the models. 80 | Through these meticulous data creation and processing efforts, BiMediX is able to excel in understanding and generating medical content across two languages. 81 | 82 | --- 83 | 84 | ## 💫 Qualitative Results 85 | 86 |
87 | Bilingual Conversation 88 | Multiple Choice Question Answering 89 |
90 | 91 | --- 92 | 93 | ## 📊 Quantitative Results 94 | 95 | The BiMediX model was evaluated across several benchmarks, demonstrating its effectiveness in medical language understanding and question answering in both English and Arabic. 96 | 97 | **Medical Benchmarks Used for Evaluation:** 98 | - *PubMedQA*: A dataset for question answering from biomedical research papers, requiring reasoning over biomedical contexts. 99 | - *MedMCQA*: Multiple-choice questions from Indian medical entrance exams, covering a wide range of medical subjects. 100 | - *MedQA*: Questions from US and other medical board exams, testing specific knowledge and patient case understanding. 101 | - *Medical MMLU*: A compilation of questions from various medical subjects, requiring broad medical knowledge. 102 | 103 | 104 | ### Bilingual Benchmark 105 | 106 | | **Model** | **CKG** | **CBio** | **CMed** | **MedGen** | **ProMed** | **Ana** | **MedMCQA** | **MedQA** | **PubmedQA** | **AVG** | 107 | |-----------------------------------|------------|-----------|-----------|-------------|-------------|---------|-------------|-----------|--------------|---------| 108 | | Jais-30B | 57.4 | 55.2 | 46.2 | 55.0 | 46.0 | 48.9 | 40.2 | 31.0 | 75.5 | 50.6 | 109 | | Mixtral-8x7B| 59.1 | 57.6 | 52.6 | 59.5 | 53.3 | 54.4 | 43.2 | 40.6 | 74.7 | 55.0 | 110 | | **BiMediX (Bilingual)** | **70.6** | **72.2** | **59.3** | **74.0** | **64.2** | **59.6**| **55.8** | **54.0** | **78.6** | **65.4**| 111 | 112 | BiMediX shows superior performance in bilingual (Arabic-English) evaluations, outperforming both the Mixtral-8x7B base model and Jais-30B. It demonstrated more than 10 and 15 points higher average accuracy, respectively. 113 | 114 | ### Arabic Benchmark 115 | 116 | | **Model** | **CKG** | **CBio** | **CMed** | **MedGen** | **ProMed** | **Ana** | **MedMCQA** | **MedQA** | **PubmedQA** | **AVG** | 117 | |-----------|------------|-----------|-----------|-------------|-------------|---------|-------------|-----------|--------------|---------| 118 | | Jais-30B | 52.1 | 50.7 | 40.5 | 49.0 | 39.3 | 43.0 | 37.0 | 28.8 | 74.6 | 46.1 | 119 | | BiMediX (Arabic) | 60.0 | 54.9 | **55.5** | 58.0 | **58.1** | 49.6 | 46.0 | 40.2 | 76.6 | 55.4 | 120 | | **BiMediX (Bilingual)** | **63.8** | **57.6** | 52.6 | **64.0** | 52.9 | **50.4** | **49.1** | **47.3** | **78.4** | **56.5** | 121 | 122 | In Arabic-specific evaluations, BiMediX outperforms Jais-30B in all categories, highlighting the effectiveness of the BiMed1.3M dataset and bilingual training. 123 | 124 | ### English Benchmark 125 | 126 | | **Model** | **CKG** | **CBio** | **CMed** | **MedGen** | **ProMed** | **Ana** | **MedMCQA** | **MedQA** | **PubmedQA** | **AVG** | 127 | |-----------------------|------------|-----------|-----------|-------------|-------------|---------|-------------|-----------|--------------|---------| 128 | | PMC-LLaMA-13B | 63.0 | 59.7 | 52.6 | 70.0 | 64.3 | 61.5 | 50.5 | 47.2 | 75.6 | 60.5 | 129 | | Med42-70B | 75.9 | 84.0 | 69.9 | 83.0 | 78.7 | 64.4 | 61.9 | 61.3 | 77.2 | 72.9 | 130 | | Clinical Camel-70B | 69.8 | 79.2 | 67.0 | 69.0 | 71.3 | 62.2 | 47.0 | 53.4 | 74.3 | 65.9 | 131 | | Meditron-70B | 72.3 | 82.5 | 62.8 | 77.8 | 77.9 | 62.7 | **65.1** | 60.7 | 80.0 | 71.3 | 132 | | **BiMediX** | **78.9** | **86.1** | **68.2** | **85.0** | **80.5** | **74.1**| 62.7 | **62.8** | **80.2** | **75.4** | 133 | 134 | BiMediX also excells in English medical benchmarks, surpassing other state-of-the-art models like Med42-70B and Meditron-70B in terms of average performance and efficiency. 135 | 136 | **These results underscore BiMediX's advanced capability in handling medical queries and its significant improvement over existing models in both languages, leveraging its unique bilingual dataset and training approach.** 137 | 138 | 139 | --- 140 | 141 | ## 📜 License & Citation 142 | 143 | BiMediX is released under the CC-BY-NC-SA 4.0 License. For more details, please refer to the [LICENSE](https://github.com/mbzuai-oryx/BiMediX/blob/main/LICENSE.txt) file included in this repository. 144 | 145 | ⚠️ **Warning! This release, intended for research, is not ready for clinical or commercial use.** 146 | 147 | 148 | Users are urged to employ BiMediX responsibly, especially when applying its outputs in real-world medical scenarios. 149 | It is imperative to verify the model's advice with qualified healthcare professionals and not rely on it for medical diagnoses or treatment decisions. 150 | Despite the overall advancements BiMediX shares common challenges with other language models, 151 | including hallucinations, toxicity, and stereotypes. 152 | BiMediX's medical diagnoses and recommendations are not infallible. 153 | 154 | If you use BiMediX in your research, please cite our work as follows: 155 | 156 | ``` 157 | @inproceedings{pieri-etal-2024-bimedix, 158 | title = "{B}i{M}edi{X}: Bilingual Medical Mixture of Experts {LLM}", 159 | author = "Pieri, Sara and 160 | Mullappilly, Sahal Shaji and 161 | Khan, Fahad Shahbaz and 162 | Anwer, Rao Muhammad and 163 | Khan, Salman and 164 | Baldwin, Timothy and 165 | Cholakkal, Hisham", 166 | editor = "Al-Onaizan, Yaser and 167 | Bansal, Mohit and 168 | Chen, Yun-Nung", 169 | booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2024", 170 | month = nov, 171 | year = "2024", 172 | address = "Miami, Florida, USA", 173 | publisher = "Association for Computational Linguistics", 174 | url = "https://aclanthology.org/2024.findings-emnlp.989", 175 | pages = "16984--17002", 176 | abstract = "In this paper, we introduce BiMediX, the first bilingual medical mixture of experts LLM designed for seamless interaction in both English and Arabic. Our model facilitates a wide range of medical interactions in English and Arabic, including multi-turn chats to inquire about additional details such as patient symptoms and medical history, multiple-choice question answering, and open-ended question answering. We propose a semi-automated English-to-Arabic translation pipeline with human refinement to ensure high-quality translations. We also introduce a comprehensive evaluation benchmark for Arabic medical LLMs. Furthermore, we introduce BiMed1.3M, an extensive Arabic-English bilingual instruction set that covers 1.3 Million diverse medical interactions, including 200k synthesized multi-turn doctor-patient chats, in a 1:2 Arabic-to-English ratio. Our model outperforms state-of-the-art Med42 and Meditron by average absolute gains of 2.5{\%} and 4.1{\%}, respectively, computed across multiple medical evaluation benchmarks in English, while operating at 8-times faster inference. Moreover, our BiMediX outperforms the generic Arabic-English bilingual LLM, Jais-30B, by average absolute gains of 10{\%} on our Arabic and 15{\%} on our bilingual evaluations across multiple datasets. Additionally, BiMediX exceeds the accuracy of GPT4 by 4.4{\%} in open-ended question UPHILL evaluation and largely outperforms state-of-the-art open source medical LLMs in human evaluations of multi-turn conversations. Our trained models, instruction set, and source code are available at https://github.com/mbzuai-oryx/BiMediX.", 177 | } 178 | ``` 179 | 180 | 181 | --- 182 | 183 | ## 🙏 Acknowledgements 184 | 185 | We are thankful to [Mistral AI](https://huggingface.co/mistralai) for releasing their models and [FastChat](https://github.com/lm-sys/FastChat) and [Axolotl](https://github.com/OpenAccess-AI-Collective/axolotl?tab=readme-ov-file#axolotl) for their open-source code contributions. 186 | 187 | This project is partially supported with Google Research Award titled *"A Climate Change and Sustainability Tailored Arabic LLM"*. 188 | 189 | --- 190 | 191 | [](https://github.com/mbzuai-oryx) 192 | [](https://mbzuai.ac.ae) 193 | -------------------------------------------------------------------------------- /LICENSE.txt: -------------------------------------------------------------------------------- 1 | Attribution-NonCommercial-ShareAlike 4.0 International 2 | 3 | ======================================================================= 4 | 5 | Creative Commons Corporation ("Creative Commons") is not a law firm and 6 | does not provide legal services or legal advice. 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