On the Intersection of Self-Correction and Trust in Language Models
Satyapriya Krishna
Large Language Models (LLMs) have demonstrated remarkable capabilities in
performing complex cognitive tasks. However, their complexity and lack of
transparency have raised several trustworthiness concerns, including the
propagation of misinformation and toxicity. Recent research has explored the
self-correction capabilities of LLMs to enhance their performance. In this
work, we investigate whether these self-correction capabilities can be
harnessed to improve the trustworthiness of LLMs. We conduct experiments
focusing on two key aspects of trustworthiness: truthfulness and toxicity. Our
findings reveal that self-correction can lead to improvements in toxicity and
truthfulness, but the extent of these improvements varies depending on the
specific aspect of trustworthiness and the nature of the task. Interestingly,
our study also uncovers instances of "self-doubt" in LLMs during the
self-correction process, introducing a new set of challenges that need to be
addressed.