Transductive Learning for Textual Few-Shot Classification: Conclusions
by Transduction University PapersMarch 1st, 2024
Too Long; Didn't Read
Few-shot classification involves training a model to perform a new classification task with a handful of labeled data.
This paper is available on arxiv under CC 4.0 license.
Authors:
(1) Pierre Colombo, Equall, Paris, France & MICS, CentraleSupelec, Universite Paris-Saclay, France;
(2) Victor Pellegrain, IRT SystemX Saclay, France & France & MICS, CentraleSupelec, Universite Paris-Saclay, France;
(3) Malik Boudiaf, ÉTS Montreal, LIVIA, ILLS, Canada;
(4) Victor Storchan, Mozilla.ai, Paris, France;
(5) Myriam Tami, MICS, CentraleSupelec, Universite Paris-Saclay, France;
(6) Ismail Ben Ayed, ÉTS Montreal, LIVIA, ILLS, Canada;
(7) Celine Hudelot, MICS, CentraleSupelec, Universite Paris-Saclay, France;
(8) Pablo Piantanida, ILLS, MILA, CNRS, CentraleSupélec, Canada.
Table of Links
- Abstract & Introduction
- Related Work
- API-based Few-shot Learning
- An Enhanced Experimental Setting
- Experiments
- Conclusions
- Limitations, Acknowledgements, & References
- Appendix A: Proof of Proposition
- Appendix B: Additional Experimental Results
6 Conclusions
This paper presents a novel FSL framework that utilizes API models while meeting critical constraints of real-world applications (i.e., R1, R2, R3). This approach is particularly appealing as it shifts the computational requirements (R2), eliminating the need for heavy computations for the user and reducing the cost of embedding. To provide a better understanding, embedding over 400k sequences cost as low as 7 dollars. In this scenario, our research highlights the potential of transductive losses, which have previously been disregarded by the NLP community. A candidate loss is the FisherRao distance which is parameter-free and could serve as a simple baseline in the future.
L O A D I N G
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