Summary of Stress-testing Long-context Language Models with Lifelong Icl and Task Haystack, by Xiaoyue Xu et al.
Stress-Testing Long-Context Language Models with Lifelong ICL and Task Haystack
by Xiaoyue Xu, Qinyuan Ye, Xiang Ren
First submitted to arxiv on: 23 Jul 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 This paper introduces Lifelong ICL, a problem setting for long-context language models to learn a sequence of language tasks through in-context learning. The evaluation suite Task Haystack assesses how well these models utilize contexts in this lifelong learning scenario. The goal is to enable models to leverage relevant demonstrations, avoid distractions, and achieve test accuracies comparable to single-task learning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper shows how to teach machines to learn many new tasks over time. It’s like a computer version of human learning, where we can use what we learned before to help us with new things. The idea is that these machines should be able to pick up on the important parts of previous tasks and use them to do better on newer ones. |
