10 papers · 1 filter
Beyond Position Bias: Shifting Context Compression from Position-Driven to Semantic-Driven
Jiwei Tang, Zhijing Huang, Xinyu Zhang +5
Large Language Models (LLMs) have demonstrated exceptional performance across diverse tasks. However, their deployment in long-context scenarios faces high computational overhead a…
PoC: Performance-oriented Context Compression for Large Language Models via Performance Prediction
Runsong Zhao, Shilei Liu, Jiwei Tang +8
While context compression can mitigate the growing inference costs of Large Language Models (LLMs) by shortening contexts, existing methods that specify a target compression ratio…
COMI: Coarse-to-fine Context Compression via Marginal Information Gain
Jiwei Tang, Shilei Liu, Zhicheng Zhang +4
Large Language Models (LLMs) have demonstrated exceptional capabilities across diverse tasks. However, their deployment in long context scenarios remains hindered by computational…
Read As Human: Compressing Context via Parallelizable Close Reading and Skimming
Jiwei Tang, Shilei Liu, Zhicheng Zhang +9
Large Language Models (LLMs) demonstrate exceptional capability across diverse tasks. However, their deployment in long-context scenarios is hindered by two challenges: computation…
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…
From Token to Line: Enhancing Code Generation with a Long-Term Perspective
Tingwei Lu, Yangning Li, Liyuan Wang +6
The emergence of large language models (LLMs) has significantly promoted the development of code generation task, sparking a surge in pertinent literature. Current research is hind…