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.

We propose a method that utilizes a large language model in the task of lyrics emotion classification. We especially employ GPT-4o, which is expected to deliver high performance to conduct emotion classification of lyrics, where we propose a few-shot prompt of GPT-4o for classification into 6 classes. We developed a dataset of 181 lyrics categorized into six classifications to evaluate GPT-4O’s performance. This dataset maintains a reasonable level of validity as it was carefully curated by the first author, then independently reclassified by six annotators, with final classifications determined through majority voting. Performing detailed classification, we achieved over 75% classification performance for the total accuracy. This accuracy was achieved through performing extractive summarization of the lyrics into optimal number of characters. In other words, rather than feeding the collected lyrics directly into GPT-4o, we implemented a two-stage process where GPT-4o first automatically summarizes the lyrics before they are used. Using a confusion matrix, we analyzed the tendencies of classification errors and showed the possibility of improving the total accuracy.

This paper proposes techniques of automatically discovering tips Web sites from a large collection of Web pages using a topic model and support vector machine (SVM). Tips refer to practical knowledge or expertise that is used to help accomplish certain tasks in a particular field. We designed several approaches of extracting features with respect to domain names based on their distribution among Web pages and candidate tips Web sites. In addition, search engine suggests, the query keywords used to fetch Web pages from the search engine are also considered to present patterns that can be potential features. It was discovered from our dataset that domain names of tips Web sites (Web sites containing tips on a certain specific theme) are more likely dispersed among topics and Web pages. These domain names also tend to correspond to a larger number of search engine suggests. This paper verifies such observed patterns by training an SVM using those extracted features. Evaluation is performed in precision and recall to measure correctness of classifying whether or not a domain name belongs to a tips Web site.

We organized a machine translation (MT) task at the Seventh NTCIR Workshop. Participating groups were requested to machine translate sentences in patent documents and also search topics for retrieving patent documents across languages. We analyzed the relationship between the accuracy of MT and its effects on the retrieval accuracy.