Matching Tasks to Objectives: Fine-Tuning and Prompt-Tuning Strategies for Encoder-Decoder Pre-trained Languag...
Summary
Prompt-based learning has emerged as a dominant paradigm in natural language processing. This study explores the impact of diverse pre-training objectives on the performance of encoder-decoder pre-trained language models across generation and question answerin...
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arXiv:2606.24841v1 (cs)
[Submitted on 23 Jun 2026]
Title:Matching Tasks to Objectives: Fine-Tuning and Prompt-Tuning Strategies for Encoder-Decoder Pre-trained Language Models
Authors:Ahmad Pouramini,Hesham Faili
View a PDF of the paper titled Matching Tasks to Objectives: Fine-Tuning and Prompt-Tuning Strategies for Encoder-Decoder Pre-trained Language Models, by Ahmad Pouramini and 1 other authors
Abstract:Prompt-based learning has emerged as a dominant paradigm in natural language processing. This study explores the impact of diverse pre-training objectives on the performance of encoder-decoder pre-trained language models across generation and question answering tasks, with a focus on commonsense knowledge retrieval and completion. We highlight the benefits of incorporating multiple objectives during both pre-training and fine-tuning stages. We introduce the Match Task to Objective (MTO) framework and methods for determining the appropriate objective for a given task. This framework offers automated methods to prepare task-related data for adaptation through unsupervised training, based on the identified objective. In the fine-tuning stage, we design novel templates that align with the objectives of the pre-training and adaptation stages. When aligned with task requirements, these strategies can achieve a performance gain of over 120\% compared to conventional methods in few-shot settings. They significantly outperform related works in few-shot settings and exceed the baseline even in full-dataset scenarios. Furthermore, we extend this approach to include prompt-tuning methodologies, providing guidance for more effective soft prompt engineering and optimization. Our strategies significantly enhance prompt-tuning performance as well. These insights hold substantial value, precisely guiding the selection and optimization of models customized for specific tasks. Code is available atthis https URL
| | | | --- | --- | | Subjects: | Artificial Intelligence (cs.AI); Computation and Language (cs.CL) | | Cite as: |arXiv:2606.24841[cs.AI] | | | (orarXiv:2606.24841v1[cs.AI] for this version) | | |https://doi.org/10.48550/arXiv.2606.24841<br>Focus to learn more<br>arXiv-issued DOI via DataCite | | Journal reference: | Appl Intell 54(20):9783-9810, 2024 | | Related DOI: |https://doi.org/10.1007/s10489-024-05660-2<br>Focus to learn more<br>DOI(s) linking to related resources |
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From: Ahmad Pouramini \[view email]
[v1] Tue, 23 Jun 2026 17:21:03 UTC (1,537 KB)
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