Exploring Recurrent-Depth Language Models for Multitask Clinical Text Processing

Key points

• Developing one model for all tasks could simplify deployment and improve knowledge transfer.1
• We evaluated whether small recurrent-depth language models can meet this need through joint fine-tuning and compared their performance with standard transformers.1
• We fine-tuned Ouro1.4B-Thinking, Ouro-2.6B-Thinking, HRM-Text-1B, Qwen3-1.7B, and Qwen3-8B on 9 datasets covering information extraction, classification, inference, and question answering, and evaluated them on four additional datasets excluded from training.1
• These findings support further study of recurrent-depth models for clinical tasks through controlled experiments to isolate architectural effects and investigate how recurrent computation influences reasoning, knowledge acquisition, and generalization.1

Sources (1)

• [1]Exploring Recurrent-Depth Language Models for Multitask Clinical Text Processing
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 20, 03:18 AM

  Clinical natural language processing includes diverse tasks such as information extraction, classification, and question answering.

  Developing one model for all tasks could simplify deployment and improve knowledge transfer.

Extractive summary: sentences quoted from the sources.