Summary of Controllable Text Generation in the Instruction-tuning Era, by Dhananjay Ashok et al.
Controllable Text Generation in the Instruction-Tuning Era
by Dhananjay Ashok, Barnabas Poczos
First submitted to arxiv on: 2 May 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 research paper explores controllable text generation using instruction-tuning and prompting paradigms. The authors compile ConGenBench, a testbed of 17 tasks, to benchmark the performance of various baselines and methods on Instruction-tuned Language Models (ILMs). Surprisingly, prompting-based approaches outperform controllable text generation methods on most datasets and tasks, highlighting the need for research on ILMs. The study finds that prompt-based approaches match human performance on stylistic tasks but lag behind on structural tasks, emphasizing the need to explore more varied constraints and challenging stylistic tasks. To facilitate this research, the authors provide an algorithm that generates constraint datasets automatically using a Large Language Model and in-context capabilities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how we can control computers to generate text. Right now, most research focuses on changing what language models learn, but this study explores a different approach called instruction-tuning and prompting. The researchers created a testbed of 17 tasks to compare different methods. They found that using prompts to guide the computer was better than controlling what it learns. This is important because it could help computers generate text that’s more like how humans write. However, there are still some things that computers struggle with, like generating text that follows certain rules. To help researchers study these challenges, the authors developed a new way to create constraint datasets without needing pre-curated ones. |
Keywords
» Artificial intelligence » Instruction tuning » Large language model » Prompt » Prompting » Text generation
