LLMBoost: Boosting LLMs with Intermediate States
Analysis
This paper introduces LLMBoost, a novel ensemble fine-tuning framework for Large Language Models (LLMs). It moves beyond treating LLMs as black boxes by leveraging their internal representations and interactions. The core innovation lies in a boosting paradigm that incorporates cross-model attention, chain training, and near-parallel inference. This approach aims to improve accuracy and reduce inference latency, offering a potentially more efficient and effective way to utilize LLMs.
Key Takeaways
- •LLMBoost is an ensemble fine-tuning framework for LLMs.
- •It leverages intermediate states and interactions between LLMs.
- •Key innovations include cross-model attention, chain training, and near-parallel inference.
- •Aims to improve accuracy and reduce inference latency.
- •Demonstrates improvements on commonsense and arithmetic reasoning tasks.
Reference / Citation
View Original"LLMBoost incorporates three key innovations: cross-model attention, chain training, and near-parallel inference."