Update rag.en.mdx with rag simulators

pages/research/rag.en.mdx

@@ -188,6 +188,7 @@ Below is a collection of research papers highlighting key insights and the lates
 
 | **Insight**  | **Reference** | **Date** |
 | ------------- | ------------- | ------------- |
+| Shows how retrieval augmentation can be used to distill language model assistants by training retrieval augmented simulators | [KAUCUS: Knowledge Augmented User Simulators for Training Language Model Assistants](https://aclanthology.org/2024.scichat-1.5)| Mar 2024 |
 | Proposes Corrective Retrieval Augmented Generation (CRAG) to improve the robustness of generation in a RAG system. The core idea is to implement a self-correct component for the retriever and improve the utilization of retrieved documents for augmenting generation. The retrieval evaluator helps to assess the overall quality of retrieved documents given a query. Using web search and optimized knowledge utilization operations can improve automatic self-correction and efficient utilization of retrieved documents. | [Corrective Retrieval Augmented Generation](https://arxiv.org/abs/2401.15884)| Jan 2024|
 | Recursively embeds, clusters, and summarizes chunks of text, constructing a tree with differing levels of summarization from the bottom up. At inference time, the proposed RAPTOR model retrieves from the tree, integrating information across lengthy documents at different levels of abstraction. | [RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval](https://arxiv.org/abs/2401.18059)| Jan 2024 |
 | A general program with multi-step interactions between LMs and retrievers to efficiently tackle multi-label classification problems. | [In-Context Learning for Extreme Multi-Label Classification](https://arxiv.org/abs/2401.12178) | Jan 2024 |
@@ -242,6 +243,7 @@ Below is a collection of research papers highlighting key insights and the lates
 
 ## References
 
+- [KAUCUS: Knowledge Augmented User Simulators for Training Language Model Assistants](https://aclanthology.org/2024.scichat-1.5)
 - [A Survey on Hallucination in Large Language Models: Principles,Taxonomy, Challenges, and Open Questions](https://arxiv.org/abs/2311.05232)
 - [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://arxiv.org/abs/2005.11401)
 - [Retrieval-augmented multimodal language modeling](https://arxiv.org/abs/2211.12561)
@@ -257,4 +259,4 @@ Below is a collection of research papers highlighting key insights and the lates
 - [Best Practices for LLM Evaluation of RAG Applications](https://www.databricks.com/blog/LLM-auto-eval-best-practices-RAG)
 - [Building Production-Ready RAG Applications](https://youtu.be/TRjq7t2Ms5I?si=gywRj82NIc-wsHcF)
 - [Evaluating RAG Part I: How to Evaluate Document Retrieval](https://www.deepset.ai/blog/rag-evaluation-retrieval)
-- [Retrieval Augmented Generation meets Reciprocal Rank Fusion and Generated Queries](https://towardsdatascience.com/forget-rag-the-future-is-rag-fusion-1147298d8ad1)
+- [Retrieval Augmented Generation meets Reciprocal Rank Fusion and Generated Queries](https://towardsdatascience.com/forget-rag-the-future-is-rag-fusion-1147298d8ad1)