Summary of Designing Informative Metrics For Few-shot Example Selection, by Rishabh Adiga et al.
Designing Informative Metrics for Few-Shot Example Selection
by Rishabh Adiga, Lakshminarayanan Subramanian, Varun Chandrasekaran
First submitted to arxiv on: 6 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: 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 Medium Difficulty summary: This paper proposes a complexity-based prompt selection approach for sequence tagging tasks, leveraging pre-trained language models (PLMs) to extract greater performance from few-shot learning. The approach utilizes sentence-level and word-level metrics to align the syntactico-semantic complexity of test sentences with that of example sentences. By avoiding dedicated model training for prompt selection, this method achieves state-of-the-art performance on few-shot Named Entity Recognition (NER) using GPT-4, resulting in a 5% absolute improvement in F1 score on the CoNLL2003 dataset. Additionally, smaller models like GPT-j-6B show large gains of up to 28.85 points in F1/Acc. The proposed approach demonstrates the potential for PLMs to excel in few-shot learning with properly formatted examples. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: This paper is about finding the best way to help computers learn new tasks quickly, using language models. The problem is that we need to choose the right “examples” to teach these models, but it’s hard to do this correctly. Researchers propose a new method that looks at how complex the sentences are and matches them with example sentences that have similar complexity. This helps the computer learn better and faster. The results show that this method works really well, especially for small language models. |
Keywords
* Artificial intelligence * F1 score * Few shot * Gpt * Named entity recognition * Ner * Prompt
