Large language models (LLM) and their chain of thought technology (CoT) have made significant progress in the field of natural language processing (NLP). This article focuses on the impact of inference chain length on CoT performance. Research shows that, within a certain range, longer reasoning chains can improve the reasoning capabilities of LLM, thereby better completing NLP tasks. The following content will elaborate on the relevant research findings and experimental results.
Large-scale language models and thought chain prompt technology have made significant progress in NLP tasks. Research reveals the critical role of inference chain length in CoT performance. Experimental results show that, within a certain range, there is a clear correlation between the length of the inference chain and the ability of large language models.
In summary, inference chain length has a significant impact on the performance of large language models. Future research can further explore the method of determining the optimal inference chain length and the relationship between chain length and model performance under different tasks. This will help to better understand and apply thinking chain prompt technology and promote the continued development of large language models in the field of NLP.