add references

README.md

@@ -20,7 +20,7 @@ Question/Answering on scientific documents using LLMs (OpenAI, Mistral, ~~LLama2
 This application is the frontend for testing the RAG (Retrieval Augmented Generation) on scientific documents, that we are developing at NIMS.
 Differently to most of the project, we focus on scientific articles. We target only the full-text using [Grobid](https://github.com/kermitt2/grobid) that provide and cleaner results than the raw PDF2Text converter (which is comparable with most of other solutions).
 
-**NER in LLM response**: The responses from the LLMs are post-processed to extract <span stype="color:yellow">physical quantities, measurements</span> and <span stype="color:blue">materials</span> mentions.
+**NER in LLM response**: The responses from the LLMs are post-processed to extract <span stype="color:yellow">physical quantities, measurements</span> (with [grobid-quantities](https://github.com/kermitt2/grobid-quantities)) and <span stype="color:blue">materials</span> mentions (with [grobid-superconductors](https://github.com/lfoppiano/grobid-superconductors)).
 
 **Demos**: 
  - (on HuggingFace spaces): https://lfoppiano-document-qa.hf.space/