Small Language Model Can Self-correct
Haixia Han Jiaqing Liang Jie Shi Qianyu He Yanghua Xiao
Generative Language Models (LMs) such as ChatGPT have exhibited remarkable
performance across various downstream tasks. Nevertheless, one of their most
prominent drawbacks is generating inaccurate or false information with a
confident tone. Previous studies have devised sophisticated pipelines and
prompts to induce large LMs to exhibit the capability for self-correction.
However, large LMs are explicitly prompted to verify and modify its answers
separately rather than completing all steps spontaneously like humans.
Moreover, these complex prompts are extremely challenging for small LMs to
follow. In this paper, we introduce the \underline{I}ntrinsic
\underline{S}elf-\underline{C}orrection (ISC) in generative language models,
aiming to correct the initial output of LMs in a self-triggered manner, even
for those small LMs with 6 billion parameters. Specifically, we devise a
pipeline for constructing self-correction data and propose Partial Answer
Masking (PAM), aiming to endow the model with the capability for intrinsic
self-correction through fine-tuning. We conduct experiments using LMs with
parameters sizes ranging from 6 billion to 13 billion in two tasks, including
commonsense reasoning and factual knowledge reasoning. Our experiments
demonstrate that the outputs generated using ISC outperform those generated
without self-correction. We believe that the output quality of even small LMs
can be further improved by empowering them with the ability to intrinsic
self-correct.