Papers by Hongchao Jiang
ReMedi: Reasoner for Medical Clinical Prediction (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing approaches to predicting future clinical outcomes from EHRs focus on enhancing medical knowledge through distillation or RAG while relying on the model’s internal ability to interpret contextual information. |
| Approach: | They propose a framework for improving clinical outcome prediction from EHR using a sample regeneration mechanism that leverages ground-truth answers as hints to enhance reasoning. |
| Outcome: | Experiments on multiple EHR prediction tasks show significant gains of up to 19.9% over state-of-the-art baselines in terms of F1 score, underscoring ReMedi’s effectiveness in real-world clinical prediction. |
CodeJudgeBench: Benchmarking LLM-as-a-Judge for Coding Tasks (2026.acl-long)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) are increasingly used to judge code, but their reliability remains poorly understood. |
| Approach: | They propose a benchmark to evaluate Large Language Models as code judges . they find that small reasoning models outperform larger non-reasoning models . |
| Outcome: | The proposed benchmark evaluates LLM-as-a-Judge models across three coding tasks. |