Bridging the gap between data distribution and model: Dynamic data distribution optimization for improving critique capabilities of large language models

Critique ability, defined as the capacity to identify and rectify flaws in text generation, is crucial for the applications of Large Language Models (LLMs). As a meta-cognitive capability, enhancing the critique ability of LLMs poses significant challenges. Recent studies have proposed improving this ability through fine-tuning on critique datasets. However, the static data distribution of existing datasets often leads to a mismatch between the training data and the diverse optimization needs of target models, thereby hindering their effectiveness. To address this issue, we introduce a novel Dynamic Iterative Data Distribution Optimization Method (DIDD) that dynamically adjusts training data distributions to align with the specific optimization requirements of target models. Specifically, DIDD detects the vulnerable data distribution of target optimization models by conducting the meta-critique on synthesized test set. The detected vulnerable data distribution are then leveraged to construct the training dataset that aligns with target model more closely, improving the effectiveness of the training dataset. Extensive experimental results across four benchmarks demonstrate that our proposed DIDD effectively alleviates the mismatch between the training dataset and target optimization models.