Summary of A Lightweight Multi Aspect Controlled Text Generation Solution For Large Language Models, by Chenyang Zhang et al.
A Lightweight Multi Aspect Controlled Text Generation Solution For Large Language Models
by Chenyang Zhang, Jiayi Lin, Haibo Tong, Bingxuan Hou, Dongyu Zhang, Jialin Li, Junli Wang
First submitted to arxiv on: 18 Oct 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 This paper investigates the performance of large language models (LLMs) in multi-aspect controllable text generation (MCTG), a task where aspect datasets are often biased and correlated. Existing approaches have exploited additional model structures and strategies, limiting adaptability to LLMs. To activate MCTG ability of LLMs, the authors propose a lightweight MCTG pipeline based on data augmentation. The pipeline addresses concerns around bias and correlations in traditional datasets by introducing augmented control attributes and sentences. These augmented datasets are feasible for instruction tuning, leading to improved performance in MCTG with a 20% accuracy rise and reduced aspect correlations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making language models better at generating text on specific topics. Right now, these models do well when they’re taught specifically how to generate text on certain subjects. But when they don’t have good teaching data, they struggle. The authors of this paper want to help language models do better in this kind of situation. They suggest a simple way to fix the problem by adding extra information to the training data. This makes the models perform 20% better and reduces the problem of generating text that’s too focused on one topic. |
Keywords
» Artificial intelligence » Data augmentation » Instruction tuning » Text generation
