Exploiting the Potential of Seq2Seq Models as Robust Few-Shot Learners

• URL: http://arxiv.org/abs/2307.14856v2
• Date: Tue, 27 Aug 2024 04:30:29 GMT
• Title: Exploiting the Potential of Seq2Seq Models as Robust Few-Shot Learners
• Authors: Jihyeon Lee, Dain Kim, Doohae Jung, Boseop Kim, Kyoung-Woon On,
• Abstract summary: We show that seq2seq models can be highly effective few-shot learners for a wide spectrum of applications. We propose two methods to more effectively elicit in-context learning ability in seq2seq models: objective-aligned prompting and a fusion-based approach.
• Score: 8.43854206194162
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In-context learning, which offers substantial advantages over fine-tuning, is predominantly observed in decoder-only models, while encoder-decoder (i.e., seq2seq) models excel in methods that rely on weight updates. Recently, a few studies have demonstrated the feasibility of few-shot learning with seq2seq models; however, this has been limited to tasks that align well with the seq2seq architecture, such as summarization and translation. Inspired by these initial studies, we provide a first-ever extensive experiment comparing the in-context few-shot learning capabilities of decoder-only and encoder-decoder models on a broad range of tasks. Furthermore, we propose two methods to more effectively elicit in-context learning ability in seq2seq models: objective-aligned prompting and a fusion-based approach. Remarkably, our approach outperforms a decoder-only model that is six times larger and exhibits significant performance improvements compared to conventional seq2seq models across a variety of settings. We posit that, with the right configuration and prompt design, seq2seq models can be highly effective few-shot learners for a wide spectrum of applications.

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