LLM Research Findings

Overview

In this section, we regularly highlight miscellaneous and interesting research findings about how to better work with large language models (LLMs). It includes new tips, insights and developments around important LLM research areas such as scaling, agents, efficiency, hallucination, architectures, prompt injection, and much more.

LLM research and AI research in general is moving fast so we hope that this resource can help both researchers and developers stay ahead of important developments. We also welcome contributions to this section if you would like to highlight an exciting finding about your research or experiments.

Research Areas

Core LLM Capabilities

• LLM Agents: Multi-agent systems and autonomous reasoning
• LLM Reasoning: Logical reasoning and problem-solving capabilities
• LLM In-Context Recall: Memory and context retention

Advanced Techniques

• RAG for LLMs: Retrieval-augmented generation approaches
• RAG Faithfulness: Evaluating RAG model reliability
• RAG Reduces Hallucination: Mitigating false information generation

Model Architecture & Efficiency

• Infini-Attention: Infinite context processing
• LLM Tokenization: Understanding tokenization and its impact
• ThoughtSculpt: Iterative reasoning approaches

Model Behavior & Safety

• Trustworthiness in LLMs: Evaluating model reliability and safety
• LM-Guided CoT: Guided reasoning techniques
• Synthetic Data: Best practices for synthetic data generation

Tools & Platforms

• What is Groq?: Understanding the Groq platform

Getting Started

Choose a research area from the list above to explore specific findings and insights. Each topic includes detailed analysis, practical implications, and references to original research papers.

Contributing

We welcome contributions from researchers and practitioners. If you have exciting findings to share, please consider contributing to help the community stay updated with the latest developments in LLM research.