1. The reported performance gain of the proposed framework is marginal when compared to the improvements introduced by simple Prompt Tuning approaches. For instance,for Table 3, out of 2.7% gain over Roberta backbone on ReTACRED, prompting tuning (i.e. HardPrompt) already achieves the gain of 1.7%.
2. The scope of the study is under-specified. It seems that the work focuses on injecting CoT- based approach to small-scale Language Models. If that is not the case, additional relevant CoT baselines for in-context learning of Large Language Models (for text-003 and ChatGPT) are missing in Table 2 and 3 (See Question A).
3. The major components of the proposed frameworks are CCL and PR. Both of them are incremental over the previous methods with minor adaptation for CoT-based prompting proposal.
2. The scope of the study is under-specified. It seems that the work focuses on injecting CoT- based approach to small-scale Language Models. If that is not the case, additional relevant CoT baselines for in-context learning of Large Language Models (for text-003 and ChatGPT) are missing in Table 2 and 3 (See Question A).