Summary of Quokka: An Open-source Large Language Model Chatbot For Material Science, by Xianjun Yang et al.
Quokka: An Open-source Large Language Model ChatBot for Material Science
by Xianjun Yang, Stephen D. Wilson, Linda Petzold
First submitted to arxiv on: 2 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computational Engineering, Finance, and Science (cs.CE)
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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 develops a specialized chatbot for materials science, utilizing the Llama-2 language model and training it on vast research articles from the S2ORC dataset. The methodology involves pre-training on over one million domain-specific papers followed by instruction-tuning to refine its capabilities. The chatbot is designed to assist researchers, educators, and students by providing instant context-aware responses to queries in materials science. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a helpful tool for the materials science community – a chatbot that answers questions quickly and accurately. It uses a special language model called Llama-2 and trains it on lots of research articles about materials science. The chatbot can help people who work with materials, like scientists and students. |
Keywords
» Artificial intelligence » Instruction tuning » Language model » Llama
