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    LLM Research

    A Curated Collection of LLM Research Papers🔗

    Welcome to the repository of knowledge where the pursuit of understanding Large Language Models (LLMs) becomes a shared adventure. This chapter is dedicated to providing you with a meticulously curated list of research papers, each accompanied by a succinct summary highlighting the core insights. Along with direct links to the original works hosted on arXiv, this collection aims to serve as a gateway to the depths of LLM research and development.

    Prepare to dive into the technical breakthroughs, innovative methodologies, and the latest findings within the realm of LLMs. Whether you are a seasoned researcher, an industry professional, or simply an AI enthusiast, this compilation is set to be an invaluable resource in your journey through the landscape of generative AI.

    Research Papers Summary and Insights🔗

    Below is the list of selected research papers that have significantly contributed to the field of Large Language Models. Each entry includes a brief summary and an arXiv link to the full paper for a comprehensive read.

    LLM Pretraining / Fine-Tuning🔗

    • [Survey] Instruction Tuning for Large Language Models

    • [RA-DIT] Retrieval-Augmented Dual Instruction Tuning

    • [Sequential Monte Carlo] Steering of LLMs using Probabilistic Programs

    LLM Agents🔗

    • [RetroFormer] Retrospective LL Agents with Policy Gradient Optimization

    LLM Optimization🔗

    • [LLM-in-a-Flash] Efficient LLM Inference with Limited Memory

    • [RoPE] RoFormer: Enhanced Transformer with Rotary Position Embedding

    • [LORA] LOw-RAnk Adaptation of LLM

    • [Speculative] Fast Inference from Transformers via Speculative Decoding

    • [GQA] Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints

    • [Multi-Heads Sharing] Fast Transformer Decoding: One Write-Head is All You Need

    • [MoE] Outrageously Large Neural Networks: The Sparsely-Gated Mixture-Of-Experts Layer

    • [MoE] Mixture-of-Experts Meets Instruction Tuning: A Winning Combination for LLM

    LLM Prompting🔗

    • [MedPrompt] Can Generalist Foundation Models Outcompete Special-Purpose Tuning?

    • [URIAL] The Unlocking Spell on Base LLMs: Rethinking Alignment via In-Context Learning

    • [CoVE] Chain-of-Verification Reduces Hallucinations in LLM Models

    LLM Benchmarks & Evaluation🔗

    • [Benchmark] Generating Benchmarks for Factuality Evaluation of Language Models

    LLM Multi-Modal / Vision🔗

    • [Point-E] A System for Generating 3D Point Clouds from Complex Prompts

    • [CLIP] Connecting text and images

    LLM Models🔗

    • [Gemini] A Family of Highly Capable Multimodal Models

    As we embark on this scholarly expedition, remember that this is just the beginning. The field of LLMs is ever-evolving, with new discoveries and insights emerging regularly. Keep this page bookmarked, and revisit often to stay updated with the latest research that shapes the future of generative AI and LLMs.