Matching Tasks to Objectives: Fine-Tuning and Prompt-Tuning Strategies for Encoder-Decoder Pre-trained Language Models

2026-06-23 · 17:21 UTC ·Ahmad Pouramini, Hesham Faili ·1 min read

Brief

The paper presents MTO, an automated framework that matches downstream tasks to suitable pre-training objectives for encoder–decoder models by using multi-objective pre-training, unsupervised adaptation, and objective-aligned fine-tuning templates. On commonsense generation and QA, the authors report >120% few-shot gains over conventional methods, consistent improvements in full-data regimes, and improved prompt-tuning; summary based on the abstract (full text not provided).

Why it matters

The paper introduces the Match Task to Objective (MTO) framework that automatically identifies suitable pre-training objectives for encoder–decoder PLMs and prepares task-related data via unsupervised adaptation, targeting generation and question-answering with emphasis on commonsense knowledge retrieval and completion.

Key details

Aligning pre-training, adaptation, and fine-tuning objectives with novel templates yields over 120% performance gain in few-shot settings versus conventional methods, outperforms related work in few-shot, and exceeds baseline performance even with full datasets.

The approach is extended to prompt-tuning (soft prompts) with guidance for prompt engineering and optimization; code is available at https://github.com/puraminy/MTO/. Authors: Ahmad Pouramini and Hesham Faili (arXiv 2026-06-23; journal: Appl Intell 54(20):9783-9810, 2024).

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 at https://github.com/puraminy/MTO/

Journal: Appl Intell 54(20):9783-9810, 2024