1 citations · 2 across the 7 of their papers we have counts for
4 papers · 1 filter
LooGLE v2: Are LLMs Ready for Real World Long Dependency Challenges?
Ziyuan He, Yuxuan Wang, Jiaqi Li +2
Large language models (LLMs) are equipped with increasingly extended context windows recently, yet their long context understanding capabilities over long dependency tasks remain f…
LIFT: A Novel Framework for Enhancing Long-Context Understanding of LLMs via Long Input Fine-Tuning
Yansheng Mao, Yufei Xu, Jiaqi Li +5
Long-context understanding remains challenging for LLMs due to limited context windows. This paper introduces Long Input Fine-Tuning (LIFT), a framework that improves the long-cont…
LIFT: Improving Long Context Understanding Through Long Input Fine-Tuning
Yansheng Mao, Jiaqi Li, Fanxu Meng +3
Long context understanding remains challenging for large language models due to their limited context windows. This paper introduces Long Input Fine-Tuning (LIFT) for long context…
LooGLE: Can Long-Context Language Models Understand Long Contexts?
Jiaqi Li, Mengmeng Wang, Zilong Zheng +1
Large language models (LLMs), despite their impressive performance in various language tasks, are typically limited to processing texts within context-window size. This limitation…