LLMs enhanced for cancer survival prediction with reasoning framework

Researchers have developed a new framework called OncoReason to improve the interpretability and accuracy of large language models (LLMs) in predicting cancer treatment outcomes. This multi-task learning approach trains LLMs to perform survival classification, time regression, and generate natural language rationales for their predictions. Experiments using LLaMa3-8B and Med42-8B models showed that Chain-of-Thought prompting and Group Relative Policy Optimization significantly enhanced predictive performance and interpretability, setting a new benchmark for trustworthy LLMs in oncology. AI

IMPACT Enhances LLM interpretability and accuracy for clinical decision support, potentially improving patient outcomes.

RANK_REASON The cluster contains a research paper detailing a new framework and methodology for LLMs in a specific domain. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

• Chain-of-Thought
• Group Relative Policy Optimization
• LLaMa3-8B
• LLMs
• Med42-8B
• MSK-CHORD dataset
• OncoReason
• Raghu Vamshi Hemadri

• paper
• model release

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