We provide awesome papers and repositories on very comprehensive topics as follows.
Comprehensive Topics
- CoT
- VLM
- Quantization
- Grounding
- Text2IMG
- Prompt Engineering
- Prompt Tuning
- Reasoning
- Robot
- Agent
- Planning
- Reinforcement Learning
- Feedback
- In-Context Learning
- Few-Shot
- Zero-Shot
- Instruction Tuning
- PEFT
- RLHF
- RAG
- Embodied
- VQA
- Hallucination
- Diffusion
- Scaling
- Context-Window
- World Model
- Memory
- Zero-Shot
- RoPE
- Speech
- Perception
- Survey
- Segmentation
- Large Action Model
- Foundation
- RoPE
- LoRA
- PPO
- DPO
We strongly recommend checking our Notion table for an interactive experience.
Getting Started with Awesome LLM Related Papers
This repository houses a plethora of research papers related to different domains of Large Language Models (LLMs). To get started with accessing these papers, follow the steps below:
Step 1: Access the Repository
Navigate to our GitHub Repository to explore an extensive collection of papers.
Step 2: Find the Relevant Paper
Within the repository, you will find a categorized list of papers based on subjects such as Zero-shot learning, World models, Visual prompts, Reinforcement learning, etc. Use the search function if you are looking for specific topics of interest.
Step 3: Read and Implement
Upon finding a paper of interest, you can access it through the provided links (most often ArXiv links) to read the entire document. Implement ideas or methodologies that appeal to you based on your individual or organizational objectives.
Code Explanation Analogy
Consider the repository as a library filled with endless knowledge on LLMs. Each book in this library represents a research paper that can educate you on specific topics within the broad realm of AI.
Now, just like any library remains organized with sections and catalogs, this repository categorizes its papers meticulously. You can easily locate the wisdom stored within by navigating through the sections (themes) and gaining insights, learning how concepts intertwine to form a comprehensive understanding of LLM applications.
Troubleshooting
If you encounter issues while browsing or accessing the resources:
- Make sure you have a stable internet connection.
- Try refreshing the page or clearing the browser cache if a link isn’t working.
- Ensure that the links you are trying are still active; repositories can be updated frequently.
- If you need further assistance, consider reaching out to the community or checking out forums related to LLM research.
- For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.
At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.
