The performance and response length characteristics of Concise Chain-of-Thought (CCoT) prompting may vary significantly across different Large Language Models (LLMs) beyond GPT-3.5 and GPT-4. While the study highlighted a substantial reduction in response length—averaging 48.70%—and maintained performance levels for these two models, the generalizability of these results to other LLMs remains uncertain.
Different LLM architectures, such as Llama 2, PaLM, and Claude, may exhibit unique response generation behaviors influenced by their training data, model size, and underlying algorithms. For instance, some models may inherently favor verbosity to ensure clarity in reasoning, while others might be optimized for brevity. Therefore, it is crucial to conduct empirical studies on a broader range of LLMs to assess how CCoT impacts both response length and accuracy across various architectures. Such research could reveal whether the benefits of CCoT are consistent or if certain models perform better with traditional verbose CoT prompting.
Additionally, the effectiveness of CCoT in reducing costs and energy consumption could also differ based on the pricing models of various LLM APIs, which typically charge per token. Understanding these dynamics will be essential for AI engineers and researchers aiming to optimize prompt engineering techniques across diverse LLMs.