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cs.CL2026

Let's Simplify Step by Step: Guiding LLM Towards Multilingual Unsupervised Proficiency-Controlled Sentence Simplification

Jingshen Zhang, Xin Ying Qiu, Lifang Lu +4

Large language models demonstrate limited capability in proficiency-controlled sentence simplification, particularly when simplifying across large readability levels. We propose a…

cs.CL2026

Rethinking Creativity Evaluation: A Critical Analysis of Existing Creativity Evaluations

Li-Chun Lu, Miri Liu, Pin-Chun Lu +3

We examine, analyze, and compare four representative creativity measures--perplexity, LLM-as-a-Judge, the Creativity Index (CI; measuring n-gram overlap with web corpora), and synt…

cs.CL2026

BILLY: Steering Large Language Models via Merging Persona Vectors for Creative Generation

Tsung-Min Pai, Jui-I Wang, Li-Chun Lu +3

Multi-LLM systems enhance the creativity of large language models by simulating human collective intelligence but suffer from significant drawbacks, such as high computational cost…

cs.CL2025

Dynamic-SUPERB Phase-2: A Collaboratively Expanding Benchmark for Measuring the Capabilities of Spoken Language Models with 180 Tasks

Chien-yu Huang, Wei-Chih Chen, Shu-wen Yang +77

Multimodal foundation models, such as Gemini and ChatGPT, have revolutionized human-machine interactions by seamlessly integrating various forms of data. Developing a universal spo…

cs.CL2024

LLM Discussion: Enhancing the Creativity of Large Language Models via Discussion Framework and Role-Play

Li-Chun Lu, Shou-Jen Chen, Tsung-Min Pai +3

Large language models (LLMs) have shown exceptional proficiency in natural language processing but often fall short of generating creative and original responses to open-ended ques…