Summary of Leveraging Long-context Large Language Models For Multi-document Understanding and Summarization in Enterprise Applications, by Aditi Godbole et al.
Leveraging Long-Context Large Language Models for Multi-Document Understanding and Summarization in Enterprise Applications
by Aditi Godbole, Jabin Geevarghese George, Smita Shandilya
First submitted to arxiv on: 27 Sep 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 explores the use of Long-context Large Language Models (LLMs) for multi-document summarization, showcasing their ability to capture extensive connections, provide cohesive summaries, and adapt to various industry domains. The authors demonstrate the workflow of multi-document summarization for deploying long-context LLMs, highlighting case studies in legal applications, enterprise functions, medical, and news domains. These case studies reveal notable enhancements in efficiency and accuracy. Technical obstacles such as dataset diversity, model scalability, and ethical considerations like bias mitigation and factual accuracy are analyzed. The paper suggests prospective research avenues to augment the functionalities and applications of long-context LLMs, establishing them as pivotal tools for transforming information processing across diverse sectors. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how computers can understand many documents and summarize what’s important. It uses special computer models that can see connections between lots of text. The researchers show how these models can be used in different areas like law, business, medicine, and news. They test the models and find they make mistakes less often and get things done faster. There are some challenges with using these models, like making sure they’re fair and accurate. But the paper suggests ways to make them better and use them in more places. |
Keywords
» Artificial intelligence » Summarization
