Summary of Instructav: Instruction Fine-tuning Large Language Models For Authorship Verification, by Yujia Hu et al.
InstructAV: Instruction Fine-tuning Large Language Models for Authorship Verification
by Yujia Hu, Zhiqiang Hu, Chun-Wei Seah, Roy Ka-Wei Lee
First submitted to arxiv on: 16 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel approach to authorship verification, called InstructAV, is introduced that utilizes Large Language Models (LLMs) and a parameter-efficient fine-tuning (PEFT) method to improve both accuracy and explainability. The approach aligns classification decisions with transparent and understandable explanations, representing a significant progression in the field of authorship verification. The paper demonstrates state-of-the-art performance on various datasets, achieving high classification accuracy coupled with enhanced explanation reliability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary InstructAV is a new way to figure out if two texts were written by the same person or not. It uses really smart language models and a special training method to make good decisions and explain why it thinks that. This approach helps solve a problem where even super smart AI systems have trouble doing this task correctly. The results show that InstructAV does a great job of getting it right and can also tell us why. |
Keywords
* Artificial intelligence * Classification * Fine tuning * Parameter efficient
