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洞察 - Instruction Tuning for Large Language Models
인스트럭션 다양성이 일반화에 미치는 결정적 영향: ONLY-IF 연구
대규모 언어 모델의 효과적인 인스트럭션 튜닝을 위해서는 데이터셋의 양보다는 다양한 도메인을 포괄하는 인스트럭션의 다양성이 중요하다.
CommonIT: A New Instruction Tuning Method for Large Language Models Based on Data Partitioning
Commonality-aware Instruction Tuning (CommonIT) improves the instruction-following abilities of large language models by grouping similar training data into batches, enhancing the model's ability to learn and generalize across various tasks.
Don't Half-listen: Key-part Information in Continual Instruction Tuning
Proposing a novel continual instruction tuning method based on Key-part Information Gain (KPIG) to improve LLMs' instruction-following and generalization abilities.
