Mar 11, 06:10 PM

RAT and Bias-Augmented Consistency Training Enhance Large Language Models' Performance and Reduce Bias

Recent research introduces innovative methods to enhance the reasoning and generation capabilities of Large Language Models (LLMs). The Retrieval Augmented Thoughts (RAT) approach involves iteratively revising a chain of thoughts with retrieved information to improve LLM performance in long-horizon tasks. Additionally, a new Bias-Augmented Consistency Training method aims to reduce biased reasoning in AI models, enhancing explainability and reducing biases.

#Large Language Models #Retrieval Augmented Thoughts

Written with ChatGPT (GPT-3).