Automatic Posology Structuration : What role for LLMs?

• URL: http://arxiv.org/abs/2506.19525v1
• Date: Tue, 24 Jun 2025 11:25:21 GMT
• Title: Automatic Posology Structuration : What role for LLMs?
• Authors: Natalia Bobkova, Laura Zanella-Calzada, Anyes Tafoughalt, Raphaël Teboul, François Plesse, Félix Gaschi,
• Abstract summary: We explore the use of Large Language Models (LLMs) to convert free-text posologies into structured formats.<n>Our results show that while prompting improves performance, only fine-tuned LLMs match the accuracy of the baseline.<n>Based on this, we propose a hybrid pipeline that routes low-confidence cases from NERL to the LLM, selecting outputs based on confidence scores.
• Score: 1.0445560141983634
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Automatically structuring posology instructions is essential for improving medication safety and enabling clinical decision support. In French prescriptions, these instructions are often ambiguous, irregular, or colloquial, limiting the effectiveness of classic ML pipelines. We explore the use of Large Language Models (LLMs) to convert free-text posologies into structured formats, comparing prompt-based methods and fine-tuning against a "pre-LLM" system based on Named Entity Recognition and Linking (NERL). Our results show that while prompting improves performance, only fine-tuned LLMs match the accuracy of the baseline. Through error analysis, we observe complementary strengths: NERL offers structural precision, while LLMs better handle semantic nuances. Based on this, we propose a hybrid pipeline that routes low-confidence cases from NERL (<0.8) to the LLM, selecting outputs based on confidence scores. This strategy achieves 91% structuration accuracy while minimizing latency and compute. Our results show that this hybrid approach improves structuration accuracy while limiting computational cost, offering a scalable solution for real-world clinical use.

Related papers

• Phishing Detection in the Gen-AI Era: Quantized LLMs vs Classical Models [1.4999444543328293]
  Phishing attacks are becoming increasingly sophisticated, underscoring the need for detection systems that strike a balance between high accuracy and computational efficiency.<n>This paper presents a comparative evaluation of traditional Machine Learning (ML), Deep Learning (DL), and quantized small- parameter Large Language Models (LLMs) for phishing detection.<n>We show that while LLMs currently underperform compared to ML and DL methods in terms of raw accuracy, they exhibit strong potential for identifying subtle, context-based phishing cues.
  arXiv  Detail & Related papers  (2025-07-10T04:01:52Z)
• Confident or Seek Stronger: Exploring Uncertainty-Based On-device LLM Routing From Benchmarking to Generalization [61.02719787737867]
  Large language models (LLMs) are increasingly deployed and democratized on edge devices.<n>One promising solution is uncertainty-based SLM routing, offloading high-stakes queries to stronger LLMs when resulting in low-confidence responses on SLM.<n>We conduct a comprehensive investigation into benchmarking and generalization of uncertainty-driven routing strategies from SLMs to LLMs over 1500+ settings.
  arXiv  Detail & Related papers  (2025-02-06T18:59:11Z)
• Adaptive Pruning for Large Language Models with Structural Importance Awareness [66.2690963378878]
  Large language models (LLMs) have significantly improved language understanding and generation capabilities.<n>LLMs are difficult to deploy on resource-constrained edge devices due to their high computational and storage resource demands.<n>We propose structurally-aware adaptive pruning (SAAP) to significantly reduce the computational and memory costs while maintaining model performance.
  arXiv  Detail & Related papers  (2024-12-19T18:08:04Z)
• Provenance: A Light-weight Fact-checker for Retrieval Augmented LLM Generation Output [49.893971654861424]
  We present a light-weight approach for detecting nonfactual outputs from retrieval-augmented generation (RAG) We compute a factuality score that can be thresholded to yield a binary decision. Our experiments show high area under the ROC curve (AUC) across a wide range of relevant open source datasets.
  arXiv  Detail & Related papers  (2024-11-01T20:44:59Z)
• Attribute Controlled Fine-tuning for Large Language Models: A Case Study on Detoxification [76.14641982122696]
  We propose a constraint learning schema for fine-tuning Large Language Models (LLMs) with attribute control. We show that our approach leads to an LLM that produces fewer inappropriate responses while achieving competitive performance on benchmarks and a toxicity detection task.
  arXiv  Detail & Related papers  (2024-10-07T23:38:58Z)
• Prompt Perturbation in Retrieval-Augmented Generation based Large Language Models [9.688626139309013]
  Retrieval-Augmented Generation is considered as a means to improve the trustworthiness of text generation from large language models. In this work, we find that the insertion of even a short prefix to the prompt leads to the generation of outputs far away from factually correct answers. We introduce a novel optimization technique called Gradient Guided Prompt Perturbation.
  arXiv  Detail & Related papers  (2024-02-11T12:25:41Z)
• Fluctuation-based Adaptive Structured Pruning for Large Language Models [44.217363567065]
  FLAP (FLuctuation-based Adaptive Structured Pruning) is a retraining-free structured pruning framework for Large Language Models. It is hardware-friendly by effectively reducing storage and enhancing inference speed.
  arXiv  Detail & Related papers  (2023-12-19T09:23:48Z)
• Assessing the Reliability of Large Language Model Knowledge [78.38870272050106]
  Large language models (LLMs) have been treated as knowledge bases due to their strong performance in knowledge probing tasks. How do we evaluate the capabilities of LLMs to consistently produce factually correct answers? We propose MOdel kNowledge relIabiliTy scORe (MONITOR), a novel metric designed to directly measure LLMs' factual reliability.
  arXiv  Detail & Related papers  (2023-10-15T12:40:30Z)
• From Quantity to Quality: Boosting LLM Performance with Self-Guided Data Selection for Instruction Tuning [52.257422715393574]
  We introduce a self-guided methodology for Large Language Models (LLMs) to autonomously discern and select cherry samples from open-source datasets. Our key innovation, the Instruction-Following Difficulty (IFD) metric, emerges as a pivotal metric to identify discrepancies between a model's expected responses and its intrinsic generation capability.
  arXiv  Detail & Related papers  (2023-08-23T09:45:29Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.