Unclear superiority of intricate self-correction schemes

Determine whether more intricate self-correction schemes for large language models necessarily yield superior overall performance across tasks such as commonsense reasoning, mathematical reasoning, and code generation.

Background

The paper introduces CorrectBench to systematically evaluate self-correction strategies in LLMs across commonsense reasoning, mathematical reasoning, and code generation. Methods are categorized into intrinsic correction, external correction, and fine-tuned correction, with additional analysis of method mixtures.

Despite various proposed self-correction methods, reported gains are inconsistent across tasks. The authors explicitly note uncertainty regarding whether increasing the complexity of self-correction approaches leads to better overall outcomes, motivating comprehensive benchmarking and analysis.