Summary of Exploring Iterative Controllable Summarization with Large Language Models, by Sangwon Ryu et al.
Exploring Iterative Controllable Summarization with Large Language Models
by Sangwon Ryu, Heejin Do, Daehee Kim, Hwanjo Yu, Dongwoo Kim, Yunsu Kim, Gary Geunbae Lee, Jungseul Ok
First submitted to arxiv on: 19 Nov 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 A recently developed type of artificial intelligence, large language models (LLMs), have shown great success in creating summaries of text. However, these models are not yet capable of precisely controlling certain aspects of the summary, such as its length or focus on specific topics. This limitation makes it difficult for users to tailor the summary to their preferences. To address this issue, researchers explored ways to improve the controllability of LLMs. They developed new methods for evaluating the performance of these models and proposed a framework called “guide-to-explain” (GTE) that enables the model to correct its mistakes and generate summaries that meet specific criteria. This approach was found to be surprisingly effective, requiring fewer iterations than other techniques. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are very good at making summaries of text. But they can’t control exactly what goes into those summaries. For example, you might want a summary to be short or focus on certain topics. The problem is that these models aren’t very good at doing this. Researchers wanted to make them better. They came up with new ways to test the models and a special way for the model to fix its mistakes. This helped the model make summaries that matched what people wanted. |
