Summary of Enhancing and Assessing Instruction-following with Fine-grained Instruction Variants, by Jiuding Yang et al.
Enhancing and Assessing Instruction-Following with Fine-Grained Instruction Variants
by Jiuding Yang, Weidong Guo, Kaitong Yang, Xiangyang Li, Yu Xu, Di Niu
First submitted to arxiv on: 17 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The abstract discusses the importance of accurately aligning Large Language Models (LLMs) with precise instructions for their application in various real-world scenarios. Current methods focus on enhancing diversity and complexity, but fail to assess LLMs’ ability to follow similar instruction variants. To address this, the authors introduce DeMoRecon, a data augmentation technique that decomposes complex instructions into simpler components, modifies them, and reconstructs new variants while preserving original context and complexity. This allows for variability critical for training and evaluating LLMs’ instruction-following precision. The authors also develop the FGIV dataset containing fine-grained instruction variants to both fine-tune and evaluate LLMs. Their findings show that LLMs fine-tuned with FGIV gain significant performance boosts on both their own and commonly used benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making Large Language Models (LLMs) better at following instructions. Right now, it’s hard to tell if an LLM will really do what you ask it to. The authors came up with a way to make training data for LLMs more varied and realistic by breaking down complex instructions into smaller parts, changing them slightly, and then putting them back together again. This helps the LLM learn how to follow different versions of the same instruction. The authors also created a big dataset called FGIV that has many variations of simple instructions. They found that when they used this data to train an LLM, it got much better at following instructions. |
Keywords
* Artificial intelligence * Data augmentation * Precision
