Multi-Task Pre-Finetuning of Lightweight Transformer Encoders for Text Classification and NER.
| Authors | Junyi Zhu 0002 et al. |
| Year | 2025 |
| Venue | EMNLP 2025 |
| Paper | View on DBLP |
Abstract
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Engineering Breakdown
Plain English
This paper investigates multi-task pre-finetuning of lightweight transformer encoders for two common NLP tasks: text classification and named entity recognition (NER). The authors explore how training a single lightweight model on multiple tasks simultaneously during the finetuning phase improves performance and efficiency compared to task-specific models, targeting scenarios where model size and inference latency matter.
Key Engineering Insight
Multi-task finetuning on lightweight transformers enables a single smaller model to match or exceed the performance of task-specific larger models, reducing deployment complexity and memory footprint while maintaining competitive accuracy across multiple NLP tasks.
Why It Matters for Engineers
Production systems often need to handle multiple NLP tasks with constrained compute budgets — mobile devices, edge servers, or cost-sensitive APIs. Instead of deploying separate models per task (multiplying your infrastructure burden), this approach lets you run one lightweight model that handles text classification and NER efficiently, directly reducing latency, memory, and operational costs.
Research Context
Prior work focused either on single-task finetuning of lightweight models or multi-task learning with standard-sized transformers. This paper bridges that gap by systematically studying whether multi-task finetuning helps lightweight encoders stay competitive. It advances the practical frontier of efficient NLP by showing that task-sharing during finetuning can substitute for model scale, enabling deployment-friendly systems without accuracy sacrifices.
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