A Short List of Important Research Papers to Understand LLM

Large Language Models (LLMs) - transformers - have revolutionised NLP and beyond. Their impact spans computer vision, computational biology, and more, showcasing the tech's versatility.

These models have advanced beyond simple chatbots, aiding in understanding complex protein structures and enhancing image recognition. Here’s a curated, chronological list of crucial papers for understanding LLMs/transformers, each with a brief why-it's-important note.

1. Neural Machine Translation by Jointly Learning to Align and Translate (2014)
   By Bahdanau, et al.

   • Introduced attention mechanisms, changing sequential data processing.

2. Attention Is All You Need (2017)
   By Vaswani, et al.

   • Laid the groundwork for transformer models, revolutionising NLP.

3. Universal Language Model Fine-tuning for Text Classification (2018)
   By Howard, et al.

   • Introduced effective transfer learning techniques for NLP tasks.

4. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2018)
   By Devlin, et al.

   • Revolutionised contextual word embeddings, improving language understanding benchmarks.

5. Improving Language Understanding by Generative Pre-Training (2018)
   By Radford, et al.

   • Demonstrated the power of generative pre-training for language understanding.

6. BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension (2019)
   By Lewis, et al.

   • Introduced BART, enhancing NLP tasks with denoising for pre-training.

7. On Layer Normalization in the Transformer Architecture (2020)
   By Xiong, et al.

   • Explored layer normalisation's role in improving transformer models' stability and performance.

8. Language Models are Few-Shot Learners (2020)
   By Brown, et al.

   • Showcased GPT-3's few-shot learning capabilities, setting new benchmarks for model versatility.

9. Scaling Language Models: Methods, Analysis & Insights from Training Gopher (2022)
   By Rae, et al.

   • Discussed scaling challenges and solutions, providing insights from training the Gopher model.

10. Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond (2023)
    By Yang, et al.

    • Offers a comprehensive survey on applying LLMs like ChatGPT in various domains, highlighting practical implications and future potential.

This list represents a journey through the evolution of LLMs, highlighting key milestones that have shaped the current landscape of AI and NLP.