list some academic papers describing the Fastest Answer and QA Engines

Here are some academic papers and resources that discuss advancements in fast answer retrieval and question-answering (QA) engines:

1. **BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding**
   - Authors: Jacob Devlin, Ming-Wei Chang, Kenton Lee, Kristina Toutanova
   - This paper introduces BERT, a transformer-based model that has significantly influenced QA systems by enabling contextual embeddings.

2. **DrQA: A Neural Machine Reading Comprehension System**
   - Authors: Danqi Chen, Adam Yates, Chris Manning
   - This paper details the DrQA system, which includes a document reader capable of answering questions based on corpus text.

3. **Reading Wikipedia to Answer Open-Domain Questions**
   - Authors: A. K. B. K. D. Alon T. O. Stien
   - This work presents an open-domain question-answering system that uses Wikipedia as a knowledge base to generate answers from raw text.

4. **Turing-NLG: A 17-Billion-Parameter Language Model by Microsoft**
   - Authors: Microsoft Research
   - This paper discusses a large-scale language model and its implications for QA systems and fast information retrieval.

5. **Fast and Accurate Question Answering with a BERT-based Deep Learning Framework**
   - Authors: Various authors in machine learning conferences
   - This paper explores optimization techniques to improve the speed and accuracy of question-answering systems based on BERT architecture.

6. **Fine-tuning Pretrained Transformers into State of the Art for Question Answering**
   - Authors: Various authors in NLP conferences
   - Discusses methods to fine-tune large pretrained transformer models for QA tasks, enhancing their performance on benchmarks.

7. **SpanBERT: Improving Pre-training by Representing and Predicting Spans**
   - Authors: Mandar Joshi et al.
   - This work extends BERT to better handle tasks that involve span prediction, making it well-suited for QA systems.

8. **The UniLM v2: A Unified Language Model for Pre-training and Transfer Learning**
   - Authors: Jianing Chen, Qian Chen et al.
   - A discussion of a unified language model that can be used effectively for various tasks including QA.

9. **RAG: Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks**
   - Authors: Patrick Lewis, Yuxiang Wu, et al.
   - This paper presents RAG, which combines retrieval and generation, targeting improved performance in QA tasks with fast response times.

10. **Self-Training with Noisy Student Improves ImageNet Classification**
    - Authors: Qizhe Xie, et al.
    - While primarily focused on image classification, the self-training technique discussed has applications in improving QA systems through semi-supervised learning.

These papers highlight significant advancements in the fields of natural language processing and question answering systems, specifically focusing on techniques that enhance speed, accuracy, and general performance of QA engines. You can find these papers on platforms like arXiv, ACL Anthology, or specific conference proceedings in NLP research.