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cs.CL2025
G2: Guided Generation for Enhanced Output Diversity in LLMs
Zhiwen Ruan, Yixia Li, Yefeng Liu +5
Large Language Models (LLMs) have demonstrated exceptional performance across diverse natural language processing tasks. However, these models exhibit a critical limitation in outp…
cs.CL2025
Marco-Bench-MIF: On Multilingual Instruction-Following Capability of Large Language Models
Bo Zeng, Chenyang Lyu, Sinuo Liu +14
Instruction-following capability has become a major ability to be evaluated for Large Language Models (LLMs). However, existing datasets, such as IFEval, are either predominantly m…
cs.CL2025
Towards Lightweight, Adaptive and Attribute-Aware Multi-Aspect Controllable Text Generation with Large Language Models
Chenyu Zhu, Yefeng Liu, Chenyang Lyu +5
Multi-aspect controllable text generation aims to control text generation in attributes from multiple aspects, making it a complex but powerful task in natural language processing.…