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This is a Experimental Study study (also classified as: Quantitative Study).

October 20, 2025Open Access

Adapting Decoder-Based Language Models for Diverse Encoder Downstream Tasks

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This paper presents Gemma Encoder, enhancing decoder models for diverse tasks, suggesting improved performance in benchmark evaluations.

Gemma Encoder demonstrates improved performance in natural language processing tasks compared to traditional architectures.
Benchmarking against GLUE and MS MARCO shows its versatility across a range of applications.
The analysis includes evaluating pooling strategies and attention mechanisms for optimal adaptation.
This adaptation allows decoder models to compete effectively in areas where encoder models typically excel.

Suganthan et al. (2025) studied this question.

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