NILE: Internal Consistency Alignment in Large Language Models

TL;DR

The paper introduces NILE, a framework that improves instruction fine-tuning datasets by aligning them with LLMs' internal knowledge, significantly boosting model performance across various evaluation datasets.

Contribution

NILE is a novel framework that elicits and leverages internal knowledge of LLMs to revise and filter datasets, enhancing the effectiveness of instruction fine-tuning.

Findings

NILE-aligned datasets achieve up to 66.6% performance gain.

Internal consistency filtering improves dataset quality and model performance.

Each component of NILE contributes to performance improvements.

Abstract

As a crucial step to enhance LLMs alignment with human intentions, Instruction Fine-Tuning (IFT) has a high demand on dataset quality. However, existing IFT datasets often contain knowledge that is inconsistent with LLMs' internal knowledge learned from the pre-training phase, which can greatly affect the efficacy of IFT. To address this issue, we introduce NILE (iNternal consIstency aLignmEnt) framework, aimed at optimizing IFT datasets to unlock LLMs' capability further. NILE operates by eliciting target pre-trained LLM's internal knowledge corresponding to instruction data. The internal knowledge is leveraged to revise the answer in IFT datasets. Additionally, we propose a novel Internal Consistency Filtering (ICF) method to filter training samples, ensuring its high consistency with LLM's internal knowledge. Our experiments demonstrate that NILE-aligned IFT datasets sharply boost LLM performance across multiple LLM ability evaluation datasets, achieving up to 66.6% gain on Arena-Hard and 68.5% on Alpaca-Eval V2. Further analysis confirms that each component of the NILE}framework contributes to these substantial performance improvements, and provides compelling evidence that dataset consistency with pre-trained internal knowledge is pivotal for maximizing LLM potential.