Identifying task groupings for multi-task learning using pointwise V-usable information

doi:10.1016/j.jbi.2025.104881

Identifying task groupings for multi-task learning using pointwise V-usable information

2025-07-16

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DOI：10.1016/j.jbi.2025.104881

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Even in the era of Large Language Models (LLMs) which are claimed to be solutions for many tasks, fine-tuning language models remains a core methodology used in deployment for a variety of reasons – computational efficiency and performance maximization among them. Fine-tuning could be single-task or multi-task joint learning where the tasks support each other thus boosting their performance. The success of multi-task learning can depend heavily on which tasks are grouped together. Naively grouping all tasks or a random set of tasks can result in negative transfer, with the multi-task models performing worse than single-task models. Though many efforts have been made to identify task groupings and to measure the relatedness among different tasks, it remains a challenging research topic to define a metric to identify the best task grouping out of a pool of many potential task combinations. We propose such a metric.

Identifying task groupings for multi-task learning using pointwise V-usable information

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