← all papers · overview

Llama-excitor: General Instruction Tuning Via Indirect Feature Interaction

Abstract

Existing methods to fine-tune LLMs, like Adapter, Prefix-tuning, and LoRA, which introduce extra modules or additional input sequences to inject new skills or knowledge, may compromise the innate abilities of LLMs. In this paper, we propose LLaMA-Excitor, a lightweight method that stimulates the LLMs' potential to better follow instructions by gradually paying more attention to worthwhile informat

Related papers

Ranked by semantic similarity — how closely each paper's abstract matches this one (100% = near-identical topic).