most citedSpeechIQ: Speech-Agentic Intelligence Quotient Across Cognitive Levels in Voice Understanding by Large Language Models

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cs.CL20251 cited

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…

cs.CL2025

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…

cs.CL2024

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…

cs.CL2024

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…

cs.CL2024

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…