Introducing INDUS: Domain-Specific Large Language Models (LLMs) for Advanced Scientific Research
Practical Solutions and Value
Large Language Models (LLMs) like INDUS, trained on specialized corpora, excel in natural language understanding and generation for scientific domains such as Earth sciences, astronomy, physics, and biology. These models bridge the gap left by universal models, offering improved performance in specialized fields.
INDUS suite includes:
- Encoder Model: Specialized in natural language understanding
- Contrastive-Learning-Based General Text Embedding Model: Enhances performance in information retrieval tasks
- Smaller Model Versions: Suitable for low-latency or resource-constrained applications
The team has also developed three benchmark datasets to advance research in interdisciplinary domains, focusing on climate change, NASA-related topics, and information retrieval within NASA content.
Key Contributions:
- Specialized Tokenizer: INDUSBPE improves model comprehension and handling of domain-specific language
- Pretrained Encoder-Only LLMs: Fine-tuned for universal sentence embeddings
- Efficient, Smaller Models: Trained using knowledge-distillation techniques
- Scientific Benchmark Datasets: CLIMATE-CHANGE NER, NASA-QA, and NASA-IR
Experimental findings demonstrate that INDUS models outperform domain-specific encoders and general-purpose models in specialized benchmarks and tasks, marking a significant advancement in AI for scientific research.
For more details, refer to the Paper and Blog.
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