Summary of Leveraging Estimated Transferability Over Human Intuition For Model Selection in Text Ranking, by Jun Bai et al.
Leveraging Estimated Transferability Over Human Intuition for Model Selection in Text Ranking
by Jun Bai, Zhuofan Chen, Zhenzi Li, Hanhua Hong, Jianfei Zhang, Chen Li, Chenghua Lin, Wenge Rong
First submitted to arxiv on: 24 Sep 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 challenge of selecting the most effective Pre-trained Language Model (PLM) for a given dataset in text ranking tasks. Current Transferability Estimation (TE) methods are primarily designed for classification tasks, which may not align well with text ranking objectives. The authors propose computing the expected rank as transferability to reflect a model’s ranking capability. They also introduce adaptive scaling of sentence embeddings to mitigate anisotropy and incorporate training dynamics. The resulting Adaptive Ranking Transferability (AiRTran) method effectively captures subtle differences between models, demonstrating significant improvements over previous TE methods, human intuition, and ChatGPT on challenging text ranking datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about finding the best way to choose a language model for a specific task. Language models are like super smart AI helpers that can understand and generate text. The problem is that there are many different language models, and it’s hard to know which one will work best for a particular job. Researchers have come up with a new way to measure how well each language model does on a task by looking at its ability to rank sentences in order of importance. This approach helps to choose the best language model for a specific task. The results show that this method is much better than others at finding the right language model, and it only takes a little bit of extra time. |
Keywords
» Artificial intelligence » Classification » Language model » Transferability
