In concurrent work closely related to ours, Zelikman et al. (2022) applies a similar methodology
to ours in regenerating the reasoning chains for incorrect examples (we denote STaR-CoT in this
paper). Although our work shares similar ideas to their approach, we differ in several key respects.
First, their approach does not involve any contextual information from the previous step. Moreover,
their approach provides the correct answer directly to the model, rather than using correctness of
answer or error reasoning steps as bootstrapping information. It is more prone to generating erroneous
reasoning chains despite arriving at the correct answer. Moreover, they continue ﬁne-tuning the
model using the corrected reasoning chains, which is impossible for LLMs with 175B parameters
(such as ChatGPT (Ouyang et al., 2022)). We conduct inference on one arithmetic reasoning dataset
GSM8K utilizing both Iter-CoT and STaR-CoT with the same questions shown in Table 4. We use the
Enhancing Chain-of-Thoughts Prompting with Iterative Bootstrapping in Large Language Models