Summary of Assisting Humans in Complex Comparisons: Automated Information Comparison at Scale, by Truman Yuen et al.
Assisting humans in complex comparisons: automated information comparison at scale
by Truman Yuen, Graham A. Watt, Yuri Lawryshyn
First submitted to arxiv on: 5 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); 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 The proposed ASC^2End system aims to overcome scalability challenges in generative large language models (LLMs) for information comparisons. By leveraging semantic text similarity and abstractive summarization techniques, the system generates evidence-supported analyses that rival human experts in knowledge domain analytics. To contextualize information and improve model reasoning, zero-shot strategies are employed to design prompts. The system’s performance is evaluated using ROUGE scoring and survey responses, demonstrating desirable results for accurate and automated information comparison at scale. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The ASC^2End system is a new tool that helps people compare information across many different topics. It uses special language models and techniques to analyze large amounts of text and find important points. The system can summarize big pieces of text into shorter versions, which makes it easier to understand and compare information. This is helpful for people who need to make decisions or take action based on lots of data. |
Keywords
» Artificial intelligence » Rouge » Summarization » Zero shot
