Phased Instruction Fine-Tuning for Large Language Models
Instruction Fine-Tuning enhances pre-trained language models from basic next-word prediction to complex instruction-following. However, existing One-off Instruction Fine-Tuning (One-off IFT) method, applied on a diverse instruction, may not effectively boost models' adherence to instructions due to the simultaneous handling of varying instruction complexities. To improve this, Phased Instruction Fine-Tuning (Phased IFT) is proposed, based on the idea that learning to follow instructions is a gradual process. It assesses instruction difficulty using GPT-4, divides the instruction data into subsets of increasing difficulty, and uptrains the model sequentially on these subsets. Experiments with Llama-2 7B/13B/70B, Llama3 8/70B and Mistral-7B models using Alpaca data show that Phased IFT significantly outperforms One-off IFT, supporting the progressive alignment hypothesis and providing a simple and efficient way to enhance large language models. Codes and datasets from our experiments are freely available at https://github.com/xubuvd/PhasedSFT.
Code (1)
Tasks
Instruction FollowingMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Unveiling the Secret Recipe: A Guide For Supervised Fine-Tuning Small LLMs
The rise of large language models (LLMs) has created a significant disparity: industrial research labs with their computational resources, expert teams, and advanced infrastructures, can effectively fine-tune LLMs, while…
MMLUInstruction-Free Tuning of Large Vision Language Models for Medical Instruction Following
Large vision language models (LVLMs) have demonstrated impressive performance across a wide range of tasks. These capabilities largely stem from visual instruction tuning, which fine-tunes models on datasets consisting o…
Visual Question AnsweringInstruction FollowingBuilding Instruction-Tuning Datasets from Human-Written Instructions with Open-Weight Large Language Models
Instruction tuning is crucial for enabling Large Language Models (LLMs) to solve real-world tasks. Prior work has shown the effectiveness of instruction-tuning data synthesized solely from LLMs, raising a fundamental que…
Demystifying Instruction Mixing for Fine-tuning Large Language Models
Instruction tuning significantly enhances the performance of large language models (LLMs) across various tasks. However, the procedure to optimizing the mixing of instruction datasets for LLM fine-tuning is still poorly …
Language ModellingLarge Language ModelScaling Instruction-Finetuned Language Models
Finetuning language models on a collection of datasets phrased as instructions has been shown to improve model performance and generalization to unseen tasks. In this paper we explore instruction finetuning with a partic…
Coreference ResolutionCross-Lingual Question AnsweringMMLUMulti-task Language Understanding+2