Geo-RAG: Gaining Insights from Unstructured Geological Documents with Large Language Models

References

1. Devlin, J., Chang, M-W., Lee, K. and Toutanova, K. [2019] BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), 4171–4186.
   [Google Scholar]
2. Lewis et al. [2020] Retrieval-augmented generation for knowledge-intensive NLP tasks.Proceedings of the 34th International Conference on Neural Information Processing Systems (NIPS’20), Article 793, 9459–9474.
   [Google Scholar]
3. Lun, C., Hewitt, T., and Hou, S. [2022] A machine learning pipeline for document extraction.First Break, Volume 40, Issue 2, Feb 2022, pp. 73–78.
   [Google Scholar]
4. Ren, S., He, K., Girshick, R. and Sun, J. [2015]. Faster R-CNN: Towards real-time object detection with region proposal networks.Advances in neural information processing systems, 28
   [Google Scholar]
5. Sanh, V., Debut, L., Chaumond, J. and Wolf, T. [2019] DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter.[online] arXiv.org.
   [Google Scholar]
6. Touvron et al. [2023] Llama 2: Open Foundation and Fine-Tuned Chat Models.[online] arXiv.org.
   [Google Scholar]
7. Wang et al. [2022] Text Embeddings by Weakly-Supervised Contrastive Pre-training.[online] arXiv.org.
   [Google Scholar]