Develop and implement an advanced retrieval$-$augmented generation $($RAG$)$ system utilizing parameter$-$efficient fine$-$tuning $($PEFT$)$ techniques to build a context$-$aware question$-$answering model. The objective is to demonstrate the effectiveness of integrating RAG with PEFT to create a highly efficient and accurate question$-$answering system. The project will culminate in a detailed implementation, results, and analysis.

Tasks:

$1.$ Perform necessary data preprocessing techniques. $[1$ Mark$]$

$2.$ Explain and implement how Named Entity Recognition $($NER$)$ can enhance data preprocessing. $[1$ Mark$]$

$3.$ Explain and implement how Part$-$of$-$Speech $($POS$)$ tagging can enhance data preprocessing. $[1$ Mark$]$

$4.$ Explain and implement how sentiment analysis can be used to analyze user questions or contexts. $[1$ Mark$]$

$5.$ Implement a dense retriever model for fetching relevant contexts. $[3$ Marks$]$

$6.$ Implement a generative model for answer generation. $[3$ Marks$]$

$7.$ Apply and fine$-$tune the generator model using Parameter$-$Efficient Fine$-$Tuning $($PEFT$).[3$ Marks$]$

$8.$ Implement a function to evaluate the model's performance using metrics such as accuracy and F$1$ score. $[1$ Mark$]$

$9.$ Analyze the results to discuss the impact of PEFT on model performance and efficiency. $[1$ Mark$]$