Papers with CTNLI
Compartmentalised Agentic Reasoning for Clinical NLI (2026.findings-acl)
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| Challenge: | Large language models produce fluent judgments for clinical natural language inference, yet fail when the decision requires the correct inferential schema rather than surface matching. |
| Approach: | They propose a compartmentalised agentic framework that routes each premise–statement pair to a reasoning family and applies a specialised solver with explicit verification and targeted refinement. |
| Outcome: | The proposed framework improves mean accuracy from 23% with direct prompting to 57%, with the largest gains on structurally demanding reasoning types. |
Dissecting Clinical Reasoning in Natural Language Inference for Large Language Models (2026.findings-acl)
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| Challenge: | Recent studies on large language models (LLMs) have demonstrated the impact of prompting strategies and fine-tuning techniques on their reasoning capabilities. |
| Approach: | They examine four classes of prompting strategies to elicit reasoning in large language models . they then construct demonstrations using a frontier model to distil multi-step reasoning capabilities into smaller models based on Low-Rank Adaptation (LoRA). |
| Outcome: | The proposed model improves in 75% of the models on MedNLI and TREC Clinical Trials. |