chore: update readme to keeptrack the flow

lib/questions_eval/README.md

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+# How to evaluate the performance of generated synthetic data?
+
+## Datasets
+
+@Simon updating ...
+
+## Metrics
+
+| Term | Definition | Formula | Interpretation |
+|---|---|---|---|
+| Coverage score | How comprehensively the summary covers the content of the original document.  | 100 − X, where X is the percentage of document generated questions that receive an "IDK" (I Don’t Know) response based on the summary. | A higher coverage score indicates that the summary captures more of the original details and is less generic. |
+| Conformity score | Whether the summary avoids contradicting the document. | It is derived by identifying the percentage of questions for which the summary’s answer is "NO" and the document’s is "YES", or vice versa, and computing 100 − X. | A higher conformity score signifies a greater alignment between the summary and the document. |
+| Consistency score | The level of non-hallucination, is based on the accuracy of factual information in the summary as compared to the document. | 100 − X, where X is the percentage of summary derived questions that are answered with an "IDK" based on the document, indicating factual discrepancies. | A higher consistency score suggests that the summary is more factual and contains fewer inaccuracies or fabrications. |
+
+## Implementation
+
+Command
+```
+python run.py -m model=llama3.1-405b-local samples=10 num_questions=5
+```
+
+Scripts:
+```
+cd ./open-nlp/lib/questions_eval
+bash/experiments/super_tiny.sh
+```
+
+## References
+- [SemScore: Evaluating LLMs with Semantic Similarity](https://huggingface.co/blog/g-ronimo/semscore)
+- [MEDIC: Towards a Comprehensive Framework for evaluating LLMs in Clinical Applications](https://arxiv.org/pdf/2409.07314)
+
+## Contributors
+- [@simonmeoni](https://github.com/simonmeoni)
+- [@honghanhh](https://github.com/honghanhh)