Summary of T3: a Novel Zero-shot Transfer Learning Framework Iteratively Training on An Assistant Task For a Target Task, by Xindi Tong et al.
T3: A Novel Zero-shot Transfer Learning Framework Iteratively Training on an Assistant Task for a Target Task
by Xindi Tong, Yujin Zhu, Shijian Fan, Liang Xu
First submitted to arxiv on: 26 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 proposes a novel zero-shot transfer learning framework called T3 to improve the performance of Large Language Models (LLMs) like GPT and LLaMA on long text summarization tasks. The T3 framework iteratively trains a baseline LLM on an assistant task that has richer data resources and shares structural or semantic similarity with the target task. In this case, question answering is used as the assistant task to improve long text summarization performance. The authors evaluate their approach on four datasets (BBC summary, NarraSum, FairytaleQA, and NLQuAD) and achieve significant improvements in ROUGE, BLEU, and Factscore compared to three baseline LLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps computers better summarize long pieces of text by teaching them a new way to learn from smaller tasks. The idea is to use the computer’s existing language skills to help it understand longer texts. To test this idea, the researchers used a special task called “question answering” to teach the computer how to summarize longer texts. They found that this approach worked well on four different sets of text and improved the computer’s ability to summarize by up to 14%. |
Keywords
» Artificial intelligence » Bleu » Gpt » Llama » Question answering » Rouge » Summarization » Transfer learning » Zero shot
