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 understood. This study categorizes instructions into three primary types: NLP downstream tasks, coding, and general chat. We explore the effects of instruction tuning on different combinations of datasets on LLM performance, and find that certain instruction types are more advantageous for specific applications but can negatively impact other areas. This work provides insights into instruction mixtures, laying the foundations for future research.
Code (1)
Tasks
Language ModellingLarge Language ModelSimilar Papers 제목 키워드 기반
LIMIT: Less Is More for Instruction Tuning Across Evaluation Paradigms
Large Language Models are traditionally finetuned on large instruction datasets. However recent studies suggest that small, high-quality datasets can suffice for general purpose instruction following. This lack of consen…
Instruction FollowingLLaVA-MoLE: Sparse Mixture of LoRA Experts for Mitigating Data Conflicts in Instruction Finetuning MLLMs
Instruction finetuning on a variety of image-text instruction data is the key to obtaining a versatile Multimodal Large Language Model (MLLM), and different configurations of the instruction data can lead to finetuned mo…
Language ModellingLarge Language ModelMixture-of-ExpertsMultimodal Large Language ModelSMART: Submodular Data Mixture Strategy for Instruction Tuning
Instruction Tuning involves finetuning a language model on a collection of instruction-formatted datasets in order to enhance the generalizability of the model to unseen tasks. Studies have shown the importance of balanc…
Language ModelingLanguage ModellingCOIG-CQIA: Quality is All You Need for Chinese Instruction Fine-tuning
Remarkable progress on English instruction tuning has facilitated the efficacy and reliability of large language models (LLMs). However, there remains a noticeable gap in instruction tuning for Chinese, where the complex…
AllAn Empirical Study of Scaling Instruct-Tuned Large Multimodal Models
Visual instruction tuning has recently shown encouraging progress with open-source large multimodal models (LMM) such as LLaVA and MiniGPT-4. However, most existing studies of open-source LMM are performed using models w…
Visual Question Answering