CoT based Few-Shot Learning of Negative Comments Filtering

In this paper, we filter negative comments on videos using large language models (LLMs). In comment filtering using LLMs such as GPT-4 (zero-shot learning), over filtering occurs frequently. This over filtering can be improved by using few-shot learning based on chain-of-thought reasoning. Few-shot learning is a strategy to improve the performance of LLMs by presenting a small number of examples to LLMs. Chain-of-thought (CoT) [18] is one of the reasoning strategy which allows LLMs to engage in more logical reasoning by generating the process of inference. In this paper, we propose and evaluate an effective method of few-shot learning based on CoT in the task of negative comment filtering. The results showed that over filtering can be effectively reduced by selecting appropriate few-shot comments from the clusters created by GPT-4, and providing them for the prompt as demonstrations of CoT.