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LLMLingua: Compressing Prompts for Accelerated Inference of Large Language Models - Summary

LLMLingua: Compressing Prompts for Accelerated Inference of Large Language Models - Summary

This paper presents a method for compressing prompts in large language models (LLMs) to accelerate model inference and reduce cost. The method involves a budget controller, a token-level iterative compression algorithm, and an instruction tuning based method for distribution alignment. Experimental

Instruction Tuning with GPT-4 - Summary

The paper presents the first attempt to use GPT-4 to generate instruction-following data for Large Language Models (LLMs) finetuning. The 52K English and Chinese instruction-following data generated by GPT-4 leads to superior zero-shot performance on new tasks compared to the instruction-following

Language Models are Few-Shot Learners - Summary

The paper discusses the limitations of pre-trained language representations in NLP systems and the need for task-specific datasets and fine-tuning. The authors show that scaling up language models greatly improves task-agnostic, few-shot performance, sometimes even reaching competitiveness with pri