LLMs Paper Study Club
The "LLMs Paper Study Club" is a comprehensive repository created by Yang Xi. It serves as a dedicated hub for documenting and studying key conference papers related to algorithms and engineering for Large Language Models (LLMs). The focus areas include multi-modality, parameter-efficient fine-tuning (PEFT), few-shot question answering (QA), retrieval-augmented generation (RAG), interpretability of language models (LMMs), agents, chain of thought (CoT), and more.
The repository is a valuable resource for anyone interested in the evolving technologies surrounding LLMs, providing insights and notes on cutting-edge academic contributions. Here's an overview of the key components and topics covered within this project:
Multi-Modal Insights
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Gemini: This segment highlights the Gemini models, which excel in understanding images, audio, video, and text. Developed by Google, these models range from Ultra to Nano, showcasing capabilities from complex reasoning tasks to memory-constrained situations.
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GPT4Video: OpenAI's evaluation of GPT-4V demonstrates its structured reasoning capabilities in various tasks such as mathematics, visual data analysis, and code generation.
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LLaVA and LLaVAR: These models explore how large language models can be adapted to process images alongside text, enhancing multimodal understanding and generation.
Progressive Enhancement Techniques
- ProTIP: Describes a progressive tool retrieval framework that improves planning in complex tasks using contrastive learning.
- Vary and Instruct-Imagen: Focus on scaling visual vocabulary and image generation with multi-modal instruction, respectively, addressing challenges in document recognition and multi-modal comprehension.
Knowledge Representation and Agents
- RAG Series: Discusses retrieval-augmented generation methods in various domains like healthcare, law, and common sense knowledge.
- LLMs Agents and CoT: Examines how LLMs can assume roles or scenarios to perform specific tasks and how they can be utilized in chain of thought processes to enhance reasoning capabilities.
Fine-Tuning and Evaluation
- PEFT Series: Centers on parameter-efficient techniques for enhancing LLMs across different tasks and applications without massive computational overhead.
- LLM Tuning and Evaluating: Provides insights into the methodologies for fine-tuning these models for task-specific performance and evaluating them for efficiency and accuracy.
Cutting-Edge Research and Development
- High-Efficiency Model Deployment: Offers strategies for deploying large models in resource-constrained environments.
- Pretraining Techniques: Covers the latest advancements in pretraining methodologies that set the stage for powerful LLM applications.
The "LLMs Paper Study Club" repository is a treasure trove for anyone looking to deepen their understanding of the latest developments in large language models. By providing a structured and detailed study of seminal papers, it empowers practitioners and researchers alike to stay abreast of the field's evolving landscape. Access to this project can serve as a comprehensive guide for those keen to explore the theoretical underpinnings and practical applications of LLMs across various domains.