Mar 14, 06:30 PM

Researchers Develop RAT Strategy for Long-Horizon Reasoning; RAG Enhances LLMs with Contextually Relevant Info Using Argilla Trainer and Rerankers

Researchers have developed Retrieval Augmented Thoughts (RAT), combining Chain of Thought (CoT) Prompting and Retrieval Augmented Generation (RAG) for long-horizon reasoning and generation tasks. RAG enhances Language Models (LLMs) with contextually relevant information. Tools like Argilla Trainer and rerankers aim to optimize RAG models for improved performance.

#Retrieval Augmented Thoughts #Chain of Thought #CoT #Prompting #Retrieval Augmented Generation #Language Models #Argilla Trainer

Written with ChatGPT (GPT-3).