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    <abstract>We describe our system for Subtask 4 (Evidence Alignment) of the ArchEHR-QA 2026 shared task, which requires aligning each sentence of a clinician-authored answer to the supporting sentence(s) in a clinical note excerpt derived from MIMIC. The task is challenging due to many-to-many alignment structure, answer sentences with no note support, and the semantic gap between clinical note language and answer paraphrases. We explore two approaches: few-shot chain-of-thought prompting with Qwen2.5-7B-Instruct and LoRA fine-tuning of a cross-encoder with combined InfoNCE and BCE loss. Our best system achieves a micro F1 of 67.93 on the test set.</abstract>
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%0 Conference Proceedings
%T Razreshili at ArchEHR-QA 2026: Evidence Alignment via LLM Prompting and Cross-Encoder Fine-tuning
%A Zemchyk, Arina
%Y Gupta, Deepak
%Y Thompson, Paul
%Y Ananiadou, Sophia
%Y Demner-Fushman, Dina
%S Proceedings of the Third Workshop on Patient-Oriented Language Processing (CL4Health) @ LREC 2026
%D 2026
%8 May
%I ELRA Language Resources Association (ELRA)
%C Palma, Mallorca (Spain)
%F zemchyk-2026-razreshili
%X We describe our system for Subtask 4 (Evidence Alignment) of the ArchEHR-QA 2026 shared task, which requires aligning each sentence of a clinician-authored answer to the supporting sentence(s) in a clinical note excerpt derived from MIMIC. The task is challenging due to many-to-many alignment structure, answer sentences with no note support, and the semantic gap between clinical note language and answer paraphrases. We explore two approaches: few-shot chain-of-thought prompting with Qwen2.5-7B-Instruct and LoRA fine-tuning of a cross-encoder with combined InfoNCE and BCE loss. Our best system achieves a micro F1 of 67.93 on the test set.
%R 10.63317/5mop8iu8k9ej
%U https://aclanthology.org/2026.cl4health-1.49/
%U https://doi.org/10.63317/5mop8iu8k9ej
%P 524-529

Markdown (Informal)

[Razreshili at ArchEHR-QA 2026: Evidence Alignment via LLM Prompting and Cross-Encoder Fine-tuning](https://aclanthology.org/2026.cl4health-1.49/) (Zemchyk, CL4Health 2026)

• Razreshili at ArchEHR-QA 2026: Evidence Alignment via LLM Prompting and Cross-Encoder Fine-tuning (Zemchyk, CL4Health 2026)

ACL

• Arina Zemchyk. 2026. Razreshili at ArchEHR-QA 2026: Evidence Alignment via LLM Prompting and Cross-Encoder Fine-tuning. In Proceedings of the Third Workshop on Patient-Oriented Language Processing (CL4Health) @ LREC 2026, pages 524–529, Palma, Mallorca (Spain). ELRA Language Resources Association (ELRA).