Optimizing LLM Adaptation Strategies in Data-Scarce Scenarios
Optimizing LLM Adaptation Strategies in Data-Scarce Scenarios
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This document explores the adaptation of LLMs in data-scarce environments, detailing methods like SFT, LoRA, and ICL. It reviews related work, highlighting the benefits and limitations of these techniques, and discusses skill learning versus knowledge integration. The experimental framework includes datasets and training settings, addressing issues of forgetting. Results showcase comparative accuracy across various tasks, analyze catastrophic forgetting, and provide practical guidance on...