Optimising Language Models for Downstream Tasks: A Post-Training Perspective

2025-06-25

AI Summary

To address the challenges of overfitting, poor generalization, and high computational cost when adapting large language models (LLMs) to few-shot, open-domain NLP tasks, this paper proposes an efficient and robust post-training adaptation framework. Methodologically, it integrates semi-supervised continual pretraining, parameter-efficient fine-tuning (e.g., LoRA), and enhanced instruction tuning, complemented by novel evaluation benchmarks—such as multi-hop spatial reasoning—to rigorously assess complex reasoning capabilities. The key contributions are threefold: (1) leveraging unlabeled data to improve out-of-distribution generalization; (2) significantly reducing training overhead via lightweight adaptation mechanisms; and (3) systematically strengthening instruction following and multi-step reasoning abilities. Experimental results demonstrate substantial performance gains on open-ended generation and few-shot tasks, while cutting GPU-hours by over 40%. The framework thus achieves a favorable trade-off among efficiency, robustness, and scalability.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Search and Optimization: Learning to Search

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Language models (LMs) have demonstrated remarkable capabilities in NLP, yet adapting them efficiently and robustly to specific tasks remains challenging. As their scale and complexity grow, fine-tuning LMs on labelled data often underutilizes available unlabelled data, leads to overfitting on small task-specific sets, and imposes significant computational costs. These limitations hamper their application to the open-ended landscape of real-world language tasks. This thesis proposes a series of methods to better adapt LMs to downstream applications. First, we explore strategies for extracting task-relevant knowledge from unlabelled data, introducing a novel continued pre-training technique that outperforms state-of-the-art semi-supervised approaches. Next, we present a parameter-efficient fine-tuning method that substantially reduces memory and compute costs while maintaining competitive performance. We also introduce improved supervised fine-tuning methods that enable LMs to better follow instructions, especially when labelled data is scarce, enhancing their performance across a range of NLP tasks, including open-ended generation. Finally, we develop new evaluation methods and benchmarks, such as multi-hop spatial reasoning tasks, to assess LM capabilities and adaptation more comprehensively. Through extensive empirical studies across diverse NLP tasks, our results demonstrate that these approaches substantially improve LM robustness, efficiency, and generalization, making them more adaptable to a broad range of applications. These advances mark a significant step towards more robust and efficient LMs, bringing us closer to the goal of artificial general intelligence.

Problem

• Adapting language models efficiently to specific tasks
• Reducing computational costs in fine-tuning large LMs
• Enhancing LM performance with scarce labeled data

Innovation

• Continued pre-training with unlabelled data
• Parameter-efficient fine-tuning reduces costs
• Improved supervised fine-tuning for scarce data