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.

我们介绍了在生成式语言模型中的内在自我修正（ISC）方法，旨在通过自动触发方式来纠正 LM 的初始输出，即使对于具有 60 亿参数的小型 LM。我们通过构建自我修正数据和提出部分答案屏蔽（PAM）的管道来进行实验，结果表明使用 ISC 生成的输出优于没有自我修正的输出。我们相信，通过赋予它们内在自我纠正的能力，甚至小型 LM 的输出质量也可以进一步提高。