Papers with few-shot learning techniques
LAraBench: Benchmarking Arabic AI with Large Language Models (2024.eacl-long)
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Ahmed Abdelali, Hamdy Mubarak, Shammur Chowdhury, Maram Hasanain, Basel Mousi, Sabri Boughorbel, Samir Abdaljalil, Yassine El Kheir, Daniel Izham, Fahim Dalvi, Majd Hawasly, Nizi Nazar, Youssef Elshahawy, Ahmed Ali, Nadir Durrani, Natasa Milic-Frayling, Firoj Alam
| Challenge: | Recent advances in Large Language Models (LLMs) have significantly influenced the landscape of language and speech research. |
| Approach: | They used GPT-3.5-turbo, GPT-4, BLOOMZ, Jais-13b-chat, Whisper, and USM to tackle 33 distinct tasks across 61 datasets. |
| Outcome: | The proposed model outperforms SOTA models in zero-shot learning, with a few exceptions. |
Learning to Bridge Metric Spaces: Few-shot Joint Learning of Intent Detection and Slot Filling (2021.findings-acl)
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| Challenge: | Existing few-shot learning methods learn a single task each time with a few examples . but, real-world applications often contain multiple closely related tasks . |
| Approach: | They propose a few-shot joint learning scheme that captures intent and slot relationships from only a handful of examples and adapts the bridged metric space to specific few- shot domain. |
| Outcome: | The proposed model outperforms baseline models on two public datasets on intent and slot . the proposed model significantly outperformed baseline models in one and five shots settings. |
Learning Opinion Summarizers by Selecting Informative Reviews (2021.emnlp-main)
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| Challenge: | supervised summarization has been traditionally approached with unsupervised, weakly-supervised and few-shot learning techniques. |
| Approach: | They propose to combine a large dataset of opinion summaries with user reviews to form a supervised summarizer. |
| Outcome: | The proposed method improves the quality of summarization and reduces hallucinations in the summarizer. |