Summary of Exploring the Benefits Of Domain-pretraining Of Generative Large Language Models For Chemistry, by Anurag Acharya et al.
Exploring the Benefits of Domain-Pretraining of Generative Large Language Models for Chemistry
by Anurag Acharya, Shivam Sharma, Robin Cosbey, Megha Subramanian, Scott Howland, Maria Glenski
First submitted to arxiv on: 5 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A recent surge in the development of Large Language Models (LLMs) like GPT, BLOOM, and LLaMA has led to significant advancements in natural language processing (NLP). While these models excel at various tasks, they often struggle when applied to niche domains, generating incorrect or hallucinated responses. This issue is particularly concerning for scientific domains, where accuracy is paramount. To address this challenge, researchers investigated the trade-offs between leveraging off-the-shelf LLMs versus more targeted foundation models tailored to specific scientific domains. In this study, the team focused on chemistry and compared in-domain pre-trained models with open-source, off-the-shelf LLMs using zero-shot and few-shot prompting. The results show that in-domain base models perform reasonably well in a zero-shot setting and achieve impressive performance after instruction fine-tuning on chemistry-specific tasks like named entity recognition and molecular formula generation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models are super smart machines that can understand and generate human-like language. These models are great at doing many tasks, but sometimes they get stuck when asked to do something new or specific. This is a problem because we need these models to be accurate for things like science and medicine. The researchers in this study wanted to see if using a model specifically designed for chemistry would be better than using a general-purpose model. They found that the specialized model did really well, especially when it was fine-tuned with instructions on what to do. |
Keywords
» Artificial intelligence » Few shot » Fine tuning » Gpt » Llama » Named entity recognition » Natural language processing » Nlp » Prompting » Zero shot
