Enhancing and Assessing Instruction-Following with Fine-Grained Instruction Variants
- URL: http://arxiv.org/abs/2406.11301v3
- Date: Tue, 15 Oct 2024 23:26:18 GMT
- Title: Enhancing and Assessing Instruction-Following with Fine-Grained Instruction Variants
- Authors: Jiuding Yang, Weidong Guo, Kaitong Yang, Xiangyang Li, Yu Xu, Di Niu,
- Abstract summary: We introduce a technique that decomposes complex instructions into simpler sub-components, modifies these, and reconstructs them into new variants.
Based on DeMoRecon, we developed the FGIV dataset which contains fine-grained instruction variants of 1,773 seed instructions.
Our findings show that LLMs fine-tuned with FGIV will gain significant performance boost on both ours and commonly used instructions-following benchmarks.
- Score: 28.691691883519542
- License:
- Abstract: The effective alignment of Large Language Models (LLMs) with precise instructions is essential for their application in diverse real-world scenarios. Current methods focus on enhancing the diversity and complexity of training and evaluation samples, yet they fall short in accurately assessing LLMs' ability to follow similar instruction variants. We introduce an effective data augmentation technique DeMoRecon that decomposes complex instructions into simpler sub-components, modifies these, and reconstructs them into new variants, thereby preserves the original instruction's context and complexity while introducing variability, which is critical for training and evaluating LLMs' instruction-following precision. Based on DeMoRecon, we developed the FGIV dataset which contains fine-grained instruction variants of 1,773 seed instructions to both fine-tune and evaluate LLMs. Our findings show that LLMs fine-tuned with FGIV will gain significant performance boost on both ours and commonly used instructions-following benchmarks.
Related papers
- MuSC: Improving Complex Instruction Following with Multi-granularity Self-Contrastive Training [36.483136685734735]
We propose a Multi-granularity Self-Contrastive Training (MuSC) framework to improve the complex instruction alignment without relying on a stronger model.
Our method is evaluated on open-sourced models, and experiment results show our method achieves significant improvement on both complex and general instruction-following benchmarks.
arXiv Detail & Related papers (2025-02-17T08:12:49Z) - Aligning Large Language Models to Follow Instructions and Hallucinate Less via Effective Data Filtering [66.5524727179286]
NOVA is a framework designed to identify high-quality data that aligns well with the learned knowledge to reduce hallucinations.
It includes Internal Consistency Probing (ICP) and Semantic Equivalence Identification (SEI) to measure how familiar the LLM is with instruction data.
To ensure the quality of selected samples, we introduce an expert-aligned reward model, considering characteristics beyond just familiarity.
arXiv Detail & Related papers (2025-02-11T08:05:56Z) - Aligning Instruction Tuning with Pre-training [81.4748965653345]
We propose Aligning Instruction Tuning with Pre-training (AITP) to align instruction tuning with pre-training distributions.
We show consistent performance improvements with AITP on three fully open large language models (LLMs) across eight benchmarks.
arXiv Detail & Related papers (2025-01-16T08:27:40Z) - Balancing Continuous Pre-Training and Instruction Fine-Tuning: Optimizing Instruction-Following in LLMs [4.096028601599825]
Large Language Models (LLMs) for public use require continuous pre-training to remain up-to-date with the latest data.
This study aims to find the most compute-efficient strategy to gain up-to-date knowledge and instruction-following capabilities without requiring any instruction data and fine-tuning.
arXiv Detail & Related papers (2024-10-14T17:20:30Z) - Align$^2$LLaVA: Cascaded Human and Large Language Model Preference Alignment for Multi-modal Instruction Curation [56.75665429851673]
This paper introduces a novel instruction curation algorithm, derived from two unique perspectives, human and LLM preference alignment.
Experiments demonstrate that we can maintain or even improve model performance by compressing synthetic multimodal instructions by up to 90%.
arXiv Detail & Related papers (2024-09-27T08:20:59Z) - MIA-Bench: Towards Better Instruction Following Evaluation of Multimodal LLMs [47.94710556156627]
MIA-Bench is a benchmark designed to evaluate multimodal large language models (MLLMs) on their ability to strictly adhere to complex instructions.
Our benchmark comprises a diverse set of 400 image-prompt pairs, each crafted to challenge the models' compliance with layered instructions.
arXiv Detail & Related papers (2024-07-01T17:53:35Z) - Mosaic-IT: Free Compositional Data Augmentation Improves Instruction Tuning [30.82220015525281]
Mosaic Instruction Tuning (Mosaic-IT) is a human/model-free compositional data augmentation method.
Mosaic-IT randomly creates rich and diverse augmentations from existing instruction tuning data.
Our evaluations demonstrate a superior performance and training efficiency of Mosaic-IT.
arXiv Detail & Related papers (2024-05-22T04:08:20Z) - What Makes for Good Visual Instructions? Synthesizing Complex Visual Reasoning Instructions for Visual Instruction Tuning [111.01953096869947]
Visual instruction tuning is crucial for enhancing the zero-shot generalization capability of Multi-modal Large Language Models (MLLMs)
We develop a systematic approach to automatically create high-quality complex visual reasoning instructions.
Experimental results consistently demonstrate the enhanced performance of all compared MLLMs.
arXiv Detail & Related papers (2023-11-02T15:36:12Z) - Reflection-Tuning: Data Recycling Improves LLM Instruction-Tuning [79.32236399694077]
Low-quality data in the training set are usually detrimental to instruction tuning.
We propose a novel method, termed "reflection-tuning"
This approach utilizes an oracle LLM to recycle the original training data by introspecting and enhancing the quality of instructions and responses in the data.
arXiv Detail & Related papers (2023-10-18T05:13:47Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.