While large language models (LLMs) excel in open-domain question answering (QA) through in-context learning (ICL), their performance in domain-specific QA, where specialized knowledge is essential, remains suboptimal. Despite the technical feasibility of fine-tuning LLMs, the prohibitive computational cost during training and inference limits their practicality. Therefore, a more practical approach under limited computational resources is to enhance smaller language models for domain-specific QA through knowledge distillation (KD), leveraging both external domain knowledge and the knowledge embedded in LLMs. KD aims to improve student model performance by guiding it to mimic the prediction process of teacher models. With the rise of LLMs, student models can further benefit from chain of thought (CoT) rationales generated by LLMs. However, while LLMs contain extensive general knowledge, they often lack the specialized expertise required for domain-specific tasks that demand deep domain knowledge. To address this, we propose a novel KD scenario: R etrieval- E nhanced knowledge D istillation (RED), which expands the knowledge sources in KD by incorporating external knowledge. While external knowledge can enhance the model’s performance, retrieved text does not always perfectly align with the information needed to answer a given question due to the limitations in retrieval techniques and the content of external sources. Therefore, the key challenge in the retrieval-enhanced KD scenario is to effectively extract useful knowledge from external text while minimizing the impact of irrelevant information. In the proposed RED framework, LLMs are required not only to generate rationale for reasoning but also to evaluate the utility of retrieved text in facilitating reasoning. Additionally, we introduce compartment, filter, and traction mechanisms to mitigate the challenges associated with external text insertion. Experimental results demonstrate that our approach achieves consistent improvements for smaller models across four datasets in the scientific and biomedical domains. Particularly, under the perturbation setting where the retrieved text and the input text are mismatched, our method outperforms the competitive baseline of LLM inference by 9.14%, demonstrating its robustness.

Corporate organizations face increasingly complex tasks that demand effective team management. A key concept is the Shared Mental Model (SMM), which enables members to maintain performance despite limited communication. Traditional measurements rely on interviews or questionnaires, which are labor-intensive, context-specific, and unsuitable for continuous monitoring. Consequently, leaders lack practical tools to track shared cognition in real time. This paper’s empirical analysis shows that only specific categories (e.g., informative exchanges) correlate strongly with SMM, clarifying which forms of communication can influence shared cognition. This insight leads to our proposed approach, which estimates SMM from instant messaging systems like Slack. Our approach categorizes messages into communicative acts using large language models, constructs category-wise communication graphs, and applies a graph neural network for estimation. The model outperforms baselines, demonstrating the feasibility of continuous, scalable monitoring without intrusive surveys. While validated in corporate contexts, the approach extends to education, healthcare, and disaster response domains.

Existing emotion datasets have supported a wide range of NLP tasks, but most are static resources that capture language use only at the time of their creation. As a result, they cannot represent how emotional meanings shift in response to cultural and social change. To address this limitation, we present NAIST LIFE STORY, a seven-year collection of Japanese emotion-related episodes that reflect contemporary topics across multiple years. Since 2017, 1,000 crowdsourced participants per quarter have written short texts describing personal experiences associated with seven emotions: anger, anxiety, disgust, trust, joy, sadness, and surprise. The dataset currently spans 28 periods and includes gender and age information for each participant. Analyses reveal systematic differences in text length and lexical diversity across emotions, as well as clear temporal trends linked to major events such as the COVID-19 pandemic. A preliminary experiment with a large language model shows that using this dataset as contextual evidence improves time-aware emotion inference, demonstrating its value for studying the evolving relationship between emotion and language.

Patient-generated symptom expressions are linguistically diverse, often deviating from standardized medical terminology. This paper introduces the Japanese Patient Phrase Bank (JPPB), the first automatically constructed phrase-level normalization resource for Japanese patient language. JPPB introduces an embedding-based soft labeling framework that transforms traditional one-to-one dictionary mappings into graded and ambiguity-aware associations. This framework represents a shift from word-level to phrase-level normalization in Japanese. The resource covers 7,035 phrase–term pairs across 412 symptoms. Evaluation on the KEEPHA and MedNLP-SC datasets shows that soft labels consistently improve Top-1 accuracy and better approximate gold label distributions compared with hard labels. While LLM-based normalization achieved the highest scores, JPPB provides a lightweight and transparent alternative suitable for local deployment. This work demonstrates that large-scale, automatically generated phrase banks can achieve competitive performance relative to manually curated resources and serve as practical, scalable resources for medical natural language processing in Japanese.

Recent advances in large language models (LLMs) have accelerated the NLP applications in the medical and clinical domains. However, evaluations remain limited for non-English languages, such as Japanese, where clinical corpora are particularly scarce. To address this gap, we present J-ClinicalBench, a publicly available benchmark designed to reflect realistic Japanese clinical tasks. We first created 227 expert-authored clinical documents and newly constructed five datasets for core clinical tasks. Building on these datasets, J-ClinicalBench comprises nine clinical tasks spanning clinical language reasoning, generation, and understanding. We establish baseline performance on J-ClinicalBench by evaluating state-of-the-art proprietary and Japanese open-source LLMs, providing the first assessment of their utility in practical clinical scenarios. By releasing this benchmark, we aim to foster the development and evaluation of clinically applicable LLMs in Japanese healthcare, bridging the current gap between clinical NLP research and clinical practice.

Large language models (LLMs) have demonstrated extensive capabilities across various natural language processing (NLP) tasks. Knowledge graphs (KGs) harbor vast amounts of facts, furnishing external knowledge for language models. The structured knowledge extracted from KGs must undergo conversion into sentences to align with the input format required by LLMs. Previous research has commonly utilized methods such as triple conversion and template-based conversion. However, sentences converted using existing methods frequently encounter issues such as semantic incoherence, ambiguity, and unnaturalness, which distort the original intent, and deviate the sentences from the facts. Meanwhile, despite the improvement that knowledge-enhanced pre-training and prompt-tuning methods have achieved in small-scale models, they are difficult to implement for LLMs in the absence of computational resources. The advanced comprehension of LLMs facilitates in-context learning (ICL), thereby enhancing their performance without the need for additional training. In this paper, we propose a knowledge prompts generation method, GenKP, which injects knowledge into LLMs by ICL. Compared to inserting triple-conversion or templated-conversion knowledge without selection, GenKP entails generating knowledge samples using LLMs in conjunction with KGs and makes a trade-off of knowledge samples through weighted verification and BM25 ranking, reducing knowledge noise. Experimental results illustrate that incorporating knowledge prompts enhances the performance of LLMs. Furthermore, LLMs augmented with GenKP exhibit superior improvements compared to the methods utilizing triple and template-based knowledge injection.

Abstract Album art often reflects the trends and themes of the songs in a given collection, and even the identities of the musicians who produced it. It therefore plays a central role in fomenting a potential listener’s first impression of the work. As such, musicians strive to find suitable images for this purpose, and those with limited financial resources or design skills may struggle to do so. Here, we report the development of Visualyre, a deep learning–based application that generates album art images from users’ song lyrics and audio files. This tool relies on generative adversarial network models to generate images from textual input (lyrics) and style transfer models to adjust the image according to the mood of the audio. We then report the results of a user study involving 35 amateur and independent musicians who tested the system. Results suggest that Visualyre was generally well received and largely effective in its intended purpose: providing musicians with a resource for generating their own album art.

Many managers and human resource departments transfer a person in an attempt to increase the performance of an organization or team. When we define performance as team efficiency, the performance is influenced by the density of team communications. However, whether or not the candidate transferee will actually increase the density of team communications is an unknown. In this paper, we propose a new approach that estimates whether or not a person who joins a team will improve the density of team communications based on an instant messaging system (IMS). In the proposed approach, we embed people in feature space using graph neural networks. In this embedding process, we do not use the content of text communications but utilize only communication graph architectural information that expresses who is talking to whom and how often. Additionally, the proposed approach does not require a questionnaire to indicate the density of team communication as in some previous studies. In the proposed approach, we develop a machine learning model classifying whether or not a transferee will improve the density of team communications. The model classifies transferees at an accuracy of 0.57 and precision of 0.58. Since this model does not use text contents from the IMS, it is valuable in actual business situations.

Fake news provokes many societal problems; therefore, there has been extensive research on fake news detection tasks to counter it. Many fake news datasets were constructed as resources to facilitate this task. Contemporary research focuses almost exclusively on the factuality aspect of the news. However, this aspect alone is insufficient to explain “fake news,” which is a complex phenomenon that involves a wide range of issues. To fully understand the nature of each instance of fake news, it is important to observe it from various perspectives, such as the intention of the false news disseminator, the harmfulness of the news to our society, and the target of the news. We propose a novel annotation scheme with fine-grained labeling based on detailed investigations of existing fake news datasets to capture these various aspects of fake news. Using the annotation scheme, we construct and publish the first Japanese fake news dataset. The annotation scheme is expected to provide an in-depth understanding of fake news. We plan to build datasets for both Japanese and other languages using our scheme. Our Japanese dataset is published at https://hkefka385.github.io/dataset/fakenews-japanese/.

This paper presents a prototype of a chat room that detects offensive expressions in a video live streaming chat in real time.Focusing on Twitch, one of the most popular live streaming platforms, we created a dataset for the task of detecting offensive expressions.We collected 2,000 chat posts across four popular game titles with genre diversity (e.g., competitive, violent, peaceful).To make use of the similarity in offensive expressions among different social media platforms, we adopted state-of-the-art models trained on offensive expressions from Twitter for our Twitch data (i.e., transfer learning).We investigated two similarity measurements to predict the transferability, textual similarity, and game-genre similarity.Our results show that the transfer of features from social media to live streaming is effective.However, the two measurements show less correlation in the transferability prediction.

For a tourist who wishes to stroll in an unknown city, it is useful to have a recommendation of not just the shortest routes but also routes that are pleasant. This paper demonstrates a system that provides pleasant route recommendation. Currently, we focus on routes that have much green and bright views. The system measures pleasure scores by extracting colors or objects in Google Street View panorama images and re-ranks shortest paths in the order of the computed pleasure scores. The current prototype provides route recommendation for city areas in Tokyo, Kyoto and San Francisco.

Sophisticated data science techniques have recently been applied to social networks data to study social phenomena and people. Recognizing that social psychology research has witnessed a renewed interest in the notion of wisdom, with an emphasis to its contextual dimensions, this study looks at the expression of wisdom in twitter messages. Specifically, it examines the relation between wisdom in adversity and cultural influences using Twitter data from the tragic Japanese tsunami of 2011. The study employs natural language processing and data science to detect the expression of wisdom. Two categories for wisdom in adversity are used: recognition of uncertainty and change, and cognitive empathy. Data processing is applied to 1,000 annotated tweets and extended to 43,436 tweets. The results show that it is viable to study wisdom in context using social networking sites data. This short paper discusses some of the findings.

Because of the increasing popularity of social media, much information has been shared on the internet, enabling social media users to understand various real world events. Particularly, social media-based infectious disease surveillance has attracted increasing attention. In this work, we specifically examine influenza: a common topic of communication on social media. The fundamental theory of this work is that several words, such as symptom words (fever, headache, etc.), appear in advance of flu epidemic occurrence. Consequently, past word occurrence can contribute to estimation of the number of current patients. To employ such forecasting words, one can first estimate the optimal time lag for each word based on their cross correlation. Then one can build a linear model consisting of word frequencies at different time points for nowcasting and for forecasting influenza epidemics. Experimentally obtained results (using 7.7 million tweets of August 2012 – January 2016), the proposed model achieved the best nowcasting performance to date (correlation ratio 0.93) and practically sufficient forecasting performance (correlation ratio 0.91 in 1-week future prediction, and correlation ratio 0.77 in 3-weeks future prediction). This report is the first of the relevant literature to describe a model enabling prediction of future epidemics using Twitter.

Navigation in unfamiliar cities often requires frequent map checking, which is troublesome for wayfinders. We propose a novel approach for improving real-world navigation by generating short, memorable and intuitive routes. To do so we detect useful landmarks for effective route navigation. This is done by exploiting not only geographic data but also crowd footprints in Social Network Services (SNS) and Location Based Social Networks (LBSN). Specifically, we detect point, area, and line landmarks by using three indicators to measure landmark's utility: visit popularity, direct visibility, and indirect visibility. We then construct an effective route graph based on the extracted landmarks, which facilitates optimal path search. In the experiments, we show that landmark-based routes out-perform the ones created by baseline from the perspectives of the lap time and the number of references necessary to check self-positions for adjusting route directions.

Microblogging platforms such as Twitter have been recently frequently used for detecting real-time events. The spatial component, as reflected by user location, usually plays a key role in such systems. However, an often neglected source of spatial information are location mentions expressed in tweet contents. In this paper we demonstrate a novel visualization system for analyzing how Twitter users collectively talk about space and for uncovering correlations between geographical locations of Twitter users and the locations they tweet about. Our exploratory analysis is based on the development of a model of spatial information extraction and representation that allows building effective visual analytics framework for large scale datasets. We show visualization results based on half a year long dataset of Japanese tweets and a four months long collection of tweets from USA. The proposed system allows observing many space related aspects of tweet messages including the average scope of spatial attention of social media users and variances in spatial interest over time. The analytical framework we provide and the findings we outline can be valuable for scientists from diverse research areas and for any users interested in geographical and social aspects of shared online data.

On behalf of the rapid urbanization, urban areas are gradually becoming a sophisticated space where we often need to know ever evolving features to take the most of the space. Therefore, keeping up with the dynamic change of urban space would be necessary, while it usually requires lots of efforts to understand newly visiting and daily changing living spaces. In order to explore and exploit the urban complexity from crowd-sourced lifelogs, we focus on location-based social network sites. In fact, due to the proliferation of location-based social networks, we can easily acquire massive crowd-sourced lifelogs interestingly indicating their experiences in the real space. In particular, we can conduct various novel urban analytics by monitoring crowd's experiences in an unprecedented way. In this paper, we particularly attempt to exploit crowd-sourced location-based lifelogs for generating a socio-cognitive map, whose purpose is to deliver much simplified and intuitive perspective of urban space. For the purpose, we measure socio-cognitive distance among urban clusters based on human mobility to represent accessibility of urban areas based on crowd's movement. Finally, we generate a socio-cognitive map reflecting the proposed socio-cognitive distances which have computed with massive geo-tagged tweets from Twitter.