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cs.CL2024
G-DIG: Towards Gradient-based Diverse and High-quality Instruction Data Selection for Machine Translation
Xingyuan Pan, Luyang Huang, Liyan Kang +3
Large Language Models (LLMs) have demonstrated remarkable abilities in general scenarios. Instruction finetuning empowers them to align with humans in various tasks. Nevertheless,…
cs.CL2024
Retaining Key Information under High Compression Ratios: Query-Guided Compressor for LLMs
Zhiwei Cao, Qian Cao, Yu Lu +4
The growing popularity of Large Language Models has sparked interest in context compression for Large Language Models (LLMs). However, the performance of previous methods degrades…