43 citations · 109 across the 43 of their papers we have counts for
43 papers
YOMI-Bench: A Benchmark for Evaluating Kanji Reading and Phonological Understanding of LLMs for Japanese
Ryota Mibayashi, Hiroya Takamura, Hitomi Yanaka
We propose YOMI-Bench, a benchmark for evaluating kanji reading and phonological understanding of large language models (LLMs) for Japanese. In Japanese, a single kanji character o…
Low-Latency Real-Time Audio Game Commentary System via LLM-Based Parallel Text Generation
Ryota Kawamatsu, Anum Afzal, Yuki Saito +5
We present a low-latency real-time audio game commentary system that generates spoken commentary directly from live gameplay video. In this end-to-end setting, a key bottleneck is…
HOME-KGQA: A Benchmark Dataset for Multimodal Knowledge Graph Question Answering on Household Daily Activities
Shusaku Egami, Aoi Ohta, Tomoki Tsujimura +5
Large Language Models (LLMs) provide flexible natural language processing capabilities, while knowledge graphs (KGs) offer explicit and structured knowledge. Integrating these two…
Multimodal Task Interference: A Benchmark and Analysis of History-Target Mismatch in Multimodal LLMs
Masayuki Kawarada, Tatsuya Ishigaki, Hiroya Takamura
Task interference, the performance degradation caused by task switches within a single conversation, has been studied exclusively in text-only settings despite the growing prevalen…
A Comparative Study of Demonstration Selection for Practical Large Language Models-based Next POI Prediction
Ryo Nishida, Masayuki Kawarada, Tatsuya Ishigaki +2
This paper investigates demonstration selection strategies for predicting a user's next point-of-interest (POI) using large language models (LLMs), aiming to accurately forecast a…
Real-Time Generation of Game Video Commentary with Multimodal LLMs: Pause-Aware Decoding Approaches
Anum Afzal, Yuki Saito, Hiroya Takamura +5
Real-time video commentary generation provides textual descriptions of ongoing events in videos. It supports accessibility and engagement in domains such as sports, esports, and li…
Improving Interpretability of Lexical Semantic Change with Neurobiological Features
Kohei Oda, Hiroya Takamura, Kiyoaki Shirai +1
Lexical Semantic Change (LSC) is the phenomenon in which the meaning of a word change over time. Most studies on LSC focus on improving the performance of estimating the degree of…
Who Laughs with Whom? Disentangling Influential Factors in Humor Preferences across User Clusters and LLMs
Soichiro Murakami, Hidetaka Kamigaito, Hiroya Takamura +1
Humor preferences vary widely across individuals and cultures, complicating the evaluation of humor using large language models (LLMs). In this study, we model heterogeneity in hum…
Oogiri-Master: Benchmarking Humor Understanding via Oogiri
Soichiro Murakami, Hidetaka Kamigaito, Hiroya Takamura +1
Humor is a salient testbed for human-like creative thinking in large language models (LLMs). We study humor using the Japanese creative response game Oogiri, in which participants…
Tracking Temporal Dynamics of Vector Sets with Gaussian Process
Taichi Aida, Mamoru Komachi, Toshinobu Ogiso +2
Understanding the temporal evolution of sets of vectors is a fundamental challenge across various domains, including ecology, crime analysis, and linguistics. For instance, ecosyst…
QCoder Benchmark: Bridging Language Generation and Quantum Hardware through Simulator-Based Feedback
Taku Mikuriya, Tatsuya Ishigaki, Masayuki Kawarada +9
Large language models (LLMs) have increasingly been applied to automatic programming code generation. This task can be viewed as a language generation task that bridges natural lan…
QuantumBench: A Benchmark for Quantum Problem Solving
Shunya Minami, Tatsuya Ishigaki, Ikko Hamamura +6
Large language models are now integrated into many scientific workflows, accelerating data analysis, hypothesis generation, and design space exploration. In parallel with this grow…
AdParaphrase v2.0: Generating Attractive Ad Texts Using a Preference-Annotated Paraphrase Dataset
Soichiro Murakami, Peinan Zhang, Hidetaka Kamigaito +2
Identifying factors that make ad text attractive is essential for advertising success. This study proposes AdParaphrase v2.0, a dataset for ad text paraphrasing, containing human p…
Rewriting Pre-Training Data Boosts LLM Performance in Math and Code
Kazuki Fujii, Yukito Tajima, Sakae Mizuki +14
The performance of large language models (LLMs) in program synthesis and mathematical reasoning is fundamentally limited by the quality of their pre-training corpora. We introduce…
Building Instruction-Tuning Datasets from Human-Written Instructions with Open-Weight Large Language Models
Youmi Ma, Sakae Mizuki, Kazuki Fujii +12
Instruction tuning is crucial for enabling Large Language Models (LLMs) to solve real-world tasks. Prior work has shown the effectiveness of instruction-tuning data synthesized sol…
AdParaphrase: Paraphrase Dataset for Analyzing Linguistic Features toward Generating Attractive Ad Texts
Soichiro Murakami, Peinan Zhang, Hidetaka Kamigaito +2
Effective linguistic choices that attract potential customers play crucial roles in advertising success. This study aims to explore the linguistic features of ad texts that influen…
Analyzing Continuous Semantic Shifts with Diachronic Word Similarity Matrices
Hajime Kiyama, Taichi Aida, Mamoru Komachi +3
The meanings and relationships of words shift over time. This phenomenon is referred to as semantic shift. Research focused on understanding how semantic shifts occur over multiple…
Why We Build Local Large Language Models: An Observational Analysis from 35 Japanese and Multilingual LLMs
Koshiro Saito, Sakae Mizuki, Masanari Ohi +11
Why do we build local large language models (LLMs)? What should a local LLM learn from the target language? Which abilities can be transferred from other languages? Do language-spe…
GADFA: Generator-Assisted Decision-Focused Approach for Opinion Expressing Timing Identification
Chung-Chi Chen, Hiroya Takamura, Ichiro Kobayashi +2
The advancement of text generation models has granted us the capability to produce coherent and convincing text on demand. Yet, in real-life circumstances, individuals do not conti…
Distilling Analysis from Generative Models for Investment Decisions
Chung-Chi Chen, Hiroya Takamura, Ichiro Kobayashi +1
Professionals' decisions are the focus of every field. For example, politicians' decisions will influence the future of the country, and stock analysts' decisions will impact the m…
Hierarchical Organization Simulacra in the Investment Sector
Chung-Chi Chen, Hiroya Takamura, Ichiro Kobayashi +1
This paper explores designing artificial organizations with professional behavior in investments using a multi-agent simulation. The method mimics hierarchical decision-making in i…
Rehearsing Answers to Probable Questions with Perspective-Taking
Yung-Yu Shih, Ziwei Xu, Hiroya Takamura +2
Question answering (QA) has been a long-standing focus in the NLP field, predominantly addressing reading comprehension and common sense QA. However, scenarios involving the prepar…
Enhancing Financial Sentiment Analysis with Expert-Designed Hint
Chung-Chi Chen, Hiroya Takamura, Ichiro Kobayashi +1
This paper investigates the role of expert-designed hint in enhancing sentiment analysis on financial social media posts. We explore the capability of large language models (LLMs)…
Enhancing Investment Opinion Ranking through Argument-Based Sentiment Analysis
Chung-Chi Chen, Hen-Hsen Huang, Hsin-Hsi Chen +3
In the era of rapid Internet and social media platform development, individuals readily share their viewpoints online. The overwhelming quantity of these posts renders comprehensiv…