This research proposal describes the development of a virtual space platform aimed at transforming learning and collaboration in the post-corona era. This research uses technology and the Unity platform to construct a space where learning data can be collected and where students attend lectures that are close to reality.
Abstractive document summarization is one of the most important tasks in natural language processing. Many approaches based on large language models have been proposed. However, it is known that the output of LLM often includes hallucinations, such as factual inconsistency. Therefore, detecting factual inconsistencies in a summary is one important task for summarization. One solution for the detection is to utilize machine learning techniques. In general, machine learning approaches require a large number of training data to generate a robust model. However, it is difficult automatically to collect article-summary pairs with factual inconsistency from the Web because hand-written summaries on the Web are usually correct. Moreover, some existing datasets are written in English. In this paper, we propose some approaches to construct Japanese datasets with factual inconsistency automatically. For this purpose, we utilize two approaches from previous studies: FactCC and SumFC. In addition, we propose a new approach to construct summaries with exaggerated expressions, as a variety of factual inconsistencies. We call the datasets JFactCC, JSumFC, and JExnoS. For JExnoS, we utilize a two-stage approach based on GPT-4 and BART for the generation of summaries with exaggerated expressions from correct article-summary pairs. We also verify the usefulness of each constructed dataset through an experiment about factual inconsistency detection with BERT.
This study introduces an automated menu planning system for schools that leverages machine learning to optimize and streamline menu creation, focusing on nutritional guidelines, cost-effectiveness, and variety. The system comprises two main algorithms: the menu combination algorithm, which employs content-based filtering to select diverse recipes by analyzing and quantifying recipe features like color, flavor, and ingredients; and the menu optimization algorithm, which adjusts ingredient quantities to adhere to budget and nutritional standards. To evaluate the system's effectiveness, satisfaction surveys were conducted among school lunch menu planners, using a seven-point Likert scale to measure feedback on menu variety and cost-effectiveness. Results showed that the system aligns well with nutritional and budgetary requirements, with minimal errors in nutritional content and ingredient costs, and substantially reduces menu planning time.
In this paper, we handle aspect term extraction with In-Context Learning (ICL) as the target task. ICL is a method for learning Large Language Models. Instead of updating the model's parameters, prompts are provided to guide it to perform a target task. While the strength of ICL is that the model does not need to undergo additional training, the prompt includes input-output instances that cause instability in accuracy. The instances are called few-shot. Hence, selecting appropriate instances has the most important role in ICL. For this purpose, we propose a selection method with active learning. Active learning is a method of selecting instances that are useful to the model for training from unlabeled data. We regard the active learning-based approach as a sub-task for the target task. We introduce two types of sub-tasks and evaluate the effectiveness of them in the target task.
Huang et al. (2021) introduced a quantum key agreement scheme using a quantum search algorithm. They initially designed a two-party protocol, later extending it to accommodate three-or N-party scenarios. Notably, Huang et al.'s protocol avoids the need for complex quantum technologies such as entangled quantum particles. However, it is observed that Huang et al.'s protocol imposes a burden on a single participant when applied to multiparty scenarios. This paper contends that such centralization to a single participant is suboptimal for multiparty settings. Thus, our work seeks to build upon the foundations laid by Huang et al. in a manner that addresses this limitation, presenting a modified framework tailored to accommodate multiparty scenarios while distributing computational responsibilities more evenly among participants.
