Summary of Are Large Language Models In-context Personalized Summarizers? Get An Icopernicus Test Done!, by Divya Patel et al.
Are Large Language Models In-Context Personalized Summarizers? Get an iCOPERNICUS Test Done!
by Divya Patel, Pathik Patel, Ankush Chander, Sourish Dasgupta, Tanmoy Chakraborty
First submitted to arxiv on: 30 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG); Neural and Evolutionary Computing (cs.NE)
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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 paper proposes a novel framework for personalizing summarization in Large Language Models (LLMs) using In-Context Personalization Learning (ICPL). The authors highlight the limitations of existing approaches, which fail to utilize all three types of cues provided in ICPL prompts. To address this, they introduce the iCOPERNICUS framework, which uses EGISES as a comparative measure to evaluate LLMs’ responsiveness to user profile differences. The framework is tested on 17 state-of-the-art LLMs, showing that most models degrade in performance when probed with richer prompts. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper talks about how Large Language Models are good at summarizing things based on what they’ve seen before. But right now, these models don’t really understand what makes one person different from another. The authors want to fix this by creating a new way for the models to learn about people and make summaries that are more personalized. They test their idea on 17 of the best language models out there and find that most of them do worse when they’re given more information. |
Keywords
» Artificial intelligence » Summarization
