2 papers
cs.AI2026
Supervised Fine-tuning with Synthetic Rationale Data Hurts Real-World Disease Prediction
Buxin Su, Bingxuan Li, Cheng Qian +3
Supervised fine-tuning with synthetic rationale data is widely assumed to improve language model performance on clinical prediction tasks by teaching models not just what to predic…
cs.CL2026
When Does Verification Pay Off? A Closer Look at LLMs as Solution Verifiers
Jack Lu, Ryan Teehan, Jinran Jin +1
Large language models (LLMs) can act as both problem solvers and solution verifiers, where the latter select high-quality answers from a pool of solver-generated candidates. This r…