Summary of Prompt Perturbation Consistency Learning For Robust Language Models, by Yao Qiang et al.
Prompt Perturbation Consistency Learning for Robust Language Models
by Yao Qiang, Subhrangshu Nandi, Ninareh Mehrabi, Greg Ver Steeg, Anoop Kumar, Anna Rumshisky, Aram Galstyan
First submitted to arxiv on: 24 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: 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 This paper addresses the limitations of large language models (LLMs) in sequence labeling tasks, such as intent classification and slot filling, which are crucial for personal assistant systems. Current LLMs struggle to match the performance of discriminative models in these areas. The authors fine-tune large LLMs to achieve comparable results with discriminative models and analyze how input perturbations affect their performance. They identify three types of perturbations – oronyms, synonyms, and paraphrasing – that significantly impact IC-SF tasks. To mitigate this issue, the authors propose Prompt Perturbation Consistency Learning (PPCL), a regularization approach that adapts to clean and perturbed samples. PPCL outperforms data augmentation using only 10% of augmented data samples. This research contributes to improving LLMs’ robustness in real-world applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making language models better for specific tasks, like understanding what people want when they ask a question or fill out a form. Current language models are good at some things but struggle with others. The researchers tested their models and found that small changes to the input can make a big difference in how well they work. To fix this problem, they created a new way to train the models called Prompt Perturbation Consistency Learning (PPCL). PPCL works by making sure the model is consistent whether it sees normal or changed inputs. This approach helped the models recover most of their performance and even outperformed other methods using much less data. |
Keywords
* Artificial intelligence * Classification * Data augmentation * Prompt * Regularization
