Adapting Decoder-Based Language Models for Diverse Encoder Downstream Tasks

• URL: http://arxiv.org/abs/2503.02656v1
• Date: Tue, 04 Mar 2025 14:17:00 GMT
• Title: Adapting Decoder-Based Language Models for Diverse Encoder Downstream Tasks
• Authors: Paul Suganthan, Fedor Moiseev, Le Yan, Junru Wu, Jianmo Ni, Jay Han, Imed Zitouni, Enrique Alfonseca, Xuanhui Wang, Zhe Dong,
• Abstract summary: We introduce Gemma, adapting the powerful decoder model to an encoder architecture.<n>To optimize the adaptation from decoder to encoder, we analyze various pooling strategies.<n>We benchmark Gemma against established approaches on the GLUE benchmarks, and MS MARCO ranking benchmark.
• Score: 24.674661807982865
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Decoder-based transformers, while revolutionizing language modeling and scaling to immense sizes, have not completely overtaken encoder-heavy architectures in natural language processing. Specifically, encoder-only models remain dominant in tasks like classification, regression, and ranking. This is primarily due to the inherent structure of decoder-based models, which limits their direct applicability to these tasks. In this paper, we introduce Gemma Encoder, adapting the powerful Gemma decoder model to an encoder architecture, thereby unlocking its potential for a wider range of non-generative applications. To optimize the adaptation from decoder to encoder, we systematically analyze various pooling strategies, attention mechanisms, and hyperparameters (e.g., dropout rate). Furthermore, we benchmark Gemma Encoder against established approaches on the GLUE benchmarks, and MS MARCO ranking benchmark, demonstrating its effectiveness and versatility.

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