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cs.CL2025
AdmTree: Compressing Lengthy Context with Adaptive Semantic Trees
Yangning Li, Shaoshen Chen, Yinghui Li +5
The quadratic complexity of self-attention constrains Large Language Models (LLMs) in processing long contexts, a capability essential for many advanced applications. Context compr…
cs.CL2025
Teaching According to Talents! Instruction Tuning LLMs with Competence-Aware Curriculum Learning
Yangning Li, Tingwei Lu, Yinghui Li +6
Efficient instruction tuning aims to enhance the ultimate performance of large language models (LLMs) trained on a given instruction dataset. Curriculum learning as a typical data…
cs.CL2025
RAISE: Reinforced Adaptive Instruction Selection For Large Language Models
Qingsong Lv, Yangning Li, Zihua Lan +8
In the instruction fine-tuning of large language models (LLMs), it is widely recognized that a few high-quality instructions are superior to a large number of low-quality instructi…