51 citations · 181 across the 51 of their papers we have counts for
53 papers
SpeechIQ: Speech-Agentic Intelligence Quotient Across Cognitive Levels in Voice Understanding by Large Language Models
Zhen Wan, Chao-Han Huck Yang, Yahan Yu +8
We introduce Speech-based Intelligence Quotient (SIQ) as a new form of human cognition-inspired evaluation pipeline for voice understanding large language models, LLM Voice, design…
Causal Tree Extraction from Medical Case Reports: A Novel Task for Experts-like Text Comprehension
Sakiko Yahata, Zhen Wan, Fei Cheng +3
Extracting causal relationships from a medical case report is essential for comprehending the case, particularly its diagnostic process. Since the diagnostic process is regarded as…
Assessing Agentic Large Language Models in Multilingual National Bias
Qianying Liu, Katrina Qiyao Wang, Fei Cheng +1
Large Language Models have garnered significant attention for their capabilities in multilingual natural language processing, while studies on risks associated with cross biases ar…
Beyond English-Centric LLMs: What Language Do Multilingual Language Models Think in?
Chengzhi Zhong, Fei Cheng, Qianying Liu +5
In this study, we investigate whether non-English-centric LLMs, despite their strong performance, `think' in their respective dominant language: more precisely, `think' refers to h…
LLM-jp: A Cross-organizational Project for the Research and Development of Fully Open Japanese LLMs
LLM-jp, :, Akiko Aizawa +80
This paper introduces LLM-jp, a cross-organizational project for the research and development of Japanese large language models (LLMs). LLM-jp aims to develop open-source and stron…
MELD-ST: An Emotion-aware Speech Translation Dataset
Sirou Chen, Sakiko Yahata, Shuichiro Shimizu +4
Emotion plays a crucial role in human conversation. This paper underscores the significance of considering emotion in speech translation. We present the MELD-ST dataset for the emo…
J-CRe3: A Japanese Conversation Dataset for Real-world Reference Resolution
Nobuhiro Ueda, Hideko Habe, Yoko Matsui +5
Understanding expressions that refer to the physical world is crucial for such human-assisting systems in the real world, as robots that must perform actions that are expected by u…
AcTED: Automatic Acquisition of Typical Event Duration for Semi-supervised Temporal Commonsense QA
Felix Virgo, Fei Cheng, Lis Kanashiro Pereira +3
We propose a voting-driven semi-supervised approach to automatically acquire the typical duration of an event and use it as pseudo-labeled data. The human evaluation demonstrates t…
Rapidly Developing High-quality Instruction Data and Evaluation Benchmark for Large Language Models with Minimal Human Effort: A Case Study on Japanese
Yikun Sun, Zhen Wan, Nobuhiro Ueda +4
The creation of instruction data and evaluation benchmarks for serving Large language models often involves enormous human annotation. This issue becomes particularly pronounced wh…
RecMind: Japanese Movie Recommendation Dialogue with Seeker's Internal State
Takashi Kodama, Hirokazu Kiyomaru, Yin Jou Huang +1
Humans pay careful attention to the interlocutor's internal state in dialogues. For example, in recommendation dialogues, we make recommendations while estimating the seeker's inte…
Bilingual Corpus Mining and Multistage Fine-Tuning for Improving Machine Translation of Lecture Transcripts
Haiyue Song, Raj Dabre, Chenhui Chu +2
Lecture transcript translation helps learners understand online courses, however, building a high-quality lecture machine translation system lacks publicly available parallel corpo…
Dynamically Updating Event Representations for Temporal Relation Classification with Multi-category Learning
Fei Cheng, Masayuki Asahara, Ichiro Kobayashi +1
Temporal relation classification is a pair-wise task for identifying the relation of a temporal link (TLINK) between two mentions, i.e. event, time, and document creation time (DCT…
Video-Helpful Multimodal Machine Translation
Yihang Li, Shuichiro Shimizu, Chenhui Chu +2
Existing multimodal machine translation (MMT) datasets consist of images and video captions or instructional video subtitles, which rarely contain linguistic ambiguity, making visu…
Reformulating Domain Adaptation of Large Language Models as Adapt-Retrieve-Revise: A Case Study on Chinese Legal Domain
Zhen wan, Yating Zhang, Yexiang Wang +2
While large language models (LLMs) like GPT-4 have recently demonstrated astonishing zero-shot capabilities in general domain tasks, they often generate content with hallucinations…
SelfSeg: A Self-supervised Sub-word Segmentation Method for Neural Machine Translation
Haiyue Song, Raj Dabre, Chenhui Chu +2
Sub-word segmentation is an essential pre-processing step for Neural Machine Translation (NMT). Existing work has shown that neural sub-word segmenters are better than Byte-Pair En…
MultiTool-CoT: GPT-3 Can Use Multiple External Tools with Chain of Thought Prompting
Tatsuro Inaba, Hirokazu Kiyomaru, Fei Cheng +1
Large language models (LLMs) have achieved impressive performance on various reasoning tasks. To further improve the performance, we propose MultiTool-CoT, a novel framework that l…
Variable-length Neural Interlingua Representations for Zero-shot Neural Machine Translation
Zhuoyuan Mao, Haiyue Song, Raj Dabre +2
The language-independency of encoded representations within multilingual neural machine translation (MNMT) models is crucial for their generalization ability on zero-shot translati…
Exploring the Impact of Layer Normalization for Zero-shot Neural Machine Translation
Zhuoyuan Mao, Raj Dabre, Qianying Liu +3
This paper studies the impact of layer normalization (LayerNorm) on zero-shot translation (ZST). Recent efforts for ZST often utilize the Transformer architecture as the backbone,…
Comprehensive Solution Program Centric Pretraining for Table-and-Text Hybrid Numerical Reasoning
Qianying Liu, Dongsheng Yang, Wenjie Zhong +2
Numerical reasoning over table-and-text hybrid passages, such as financial reports, poses significant challenges and has numerous potential applications. Noise and irrelevant varia…
SuperDialseg: A Large-scale Dataset for Supervised Dialogue Segmentation
Junfeng Jiang, Chengzhang Dong, Sadao Kurohashi +1
Dialogue segmentation is a crucial task for dialogue systems allowing a better understanding of conversational texts. Despite recent progress in unsupervised dialogue segmentation…
GPT-RE: In-context Learning for Relation Extraction using Large Language Models
Zhen Wan, Fei Cheng, Zhuoyuan Mao +4
In spite of the potential for ground-breaking achievements offered by large language models (LLMs) (e.g., GPT-3), they still lag significantly behind fully-supervised baselines (e.…
Textual Enhanced Contrastive Learning for Solving Math Word Problems
Yibin Shen, Qianying Liu, Zhuoyuan Mao +2
Solving math word problems is the task that analyses the relation of quantities and requires an accurate understanding of contextual natural language information. Recent studies sh…
Rescue Implicit and Long-tail Cases: Nearest Neighbor Relation Extraction
Zhen Wan, Qianying Liu, Zhuoyuan Mao +3
Relation extraction (RE) has achieved remarkable progress with the help of pre-trained language models. However, existing RE models are usually incapable of handling two situations…
ComSearch: Equation Searching with Combinatorial Strategy for Solving Math Word Problems with Weak Supervision
Qianying Liu, Wenyu Guan, Jianhao Shen +2
Previous studies have introduced a weakly-supervised paradigm for solving math word problems requiring only the answer value annotation. While these methods search for correct valu…