Summary of Unsupervised Text Style Transfer Via Llms and Attention Masking with Multi-way Interactions, by Lei Pan et al.
Unsupervised Text Style Transfer via LLMs and Attention Masking with Multi-way Interactions
by Lei Pan, Yunshi Lan, Yang Li, Weining Qian
First submitted to arxiv on: 21 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 proposed paper explores unsupervised text style transfer (UTST), a critical task in Natural Language Processing (NLP) that aims to modify one stylistic aspect of a sentence without altering its semantics, syntax, or other attributes. The authors investigate the combination of attention masking and Large Language Models (LLMs) to overcome their respective shortcomings. They propose four ways of interactions, including pipeline frameworks with tuned orders, knowledge distillation from LLMs to attention masking models, and in-context learning with constructed parallel examples. Experimental results demonstrate that these multi-way interactions improve baselines in certain aspects of style strength, content preservation, and text fluency. Moreover, the authors show that prompting followed by attention masking-based revision can consistently outperform other systems, including supervised text style transfer systems, achieving new state-of-the-art (SOTA) results on Yelp-clean and Amazon-clean datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a way to change the style of writing in sentences without changing what they mean. It’s like taking a sentence written by a formal expert and making it sound like it was written by someone more casual, without changing the facts or main idea. The researchers wanted to find a way to combine two existing methods to make this process better. They tried different ways of combining these methods and found that some worked better than others. They also showed that their approach could be used to improve on previous results in a specific area of language processing. |
Keywords
» Artificial intelligence » Attention » Knowledge distillation » Natural language processing » Nlp » Prompting » Semantics » Style transfer » Supervised » Syntax » Unsupervised
