GRID: Scalable Task-Agnostic Prompt-Based Continual Learning for Language Models

• URL: http://arxiv.org/abs/2507.14725v3
• Date: Wed, 01 Oct 2025 16:07:15 GMT
• Title: GRID: Scalable Task-Agnostic Prompt-Based Continual Learning for Language Models
• Authors: Anushka Tiwari, Sayantan Pal, Rohini K. Srihari, Kaiyi Ji,
• Abstract summary: Prompt-based continual learning (CL) provides a parameter-efficient approach for adapting large language models (LLMs) across task sequences.<n>Most existing methods rely on task-aware inference and maintain a growing set of task-specific prompts.<n>We present GRID, a unified framework designed to address these challenges.
• Score: 22.312673721170388
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Prompt-based continual learning (CL) provides a parameter-efficient approach for adapting large language models (LLMs) across task sequences. However, most existing methods rely on task-aware inference and maintain a growing set of task-specific prompts, which introduces two major challenges: (1) severe performance degradation on earlier tasks under task-agnostic inference, and (2) limited scalability due to prompt memory accumulation as task sequences grow. In this paper, we present GRID, a unified framework designed to address these challenges. GRID incorporates a decoding mechanism that enhances backward transfer by leveraging representative inputs, automatic task identification, and constrained decoding. Furthermore, it employs a gradient-guided prompt selection strategy to compress less informative prompts into a single aggregated representation, ensuring scalable and memory-efficient continual learning. Extensive experiments on long-sequence and negative transfer benchmarks show that GRID improves average accuracy and backward transfer, achieves competitive forward transfer, and substantially reduces prompt memory usage.

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