Academic

Severity-Aware Weighted Loss for Arabic Medical Text Generation

arXiv:2604.06346v1 Announce Type: new Abstract: Large language models have shown strong potential for Arabic medical text generation; however, traditional fine-tuning objectives treat all medical cases uniformly, ignoring differences in clinical severity. This limitation is particularly critical in healthcare settings, where errors in severe cases contain higher clinical risk. In this work, we propose a severity-aware weighted loss for fine-tuning Arabic language models on medical complaint-response data. The method depends on soft severity probabilities to dynamically scale token-level loss contributions during optimization, thereby prioritizing clinically critical interactions without modifying model architectures. Experiments are conducted using the MAQA dataset, which provides Arabic medical complaints and trusted human responses. Severity labels and probabilistic scores are automatically derived using a fine-tuned AraBERT-based classifier and incorporated exclusively at the loss

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Ahmed Alansary, Molham Mohamed, Ali Hamdi · · 1 min read · 27 views
#cs.CL #cs.AI

arXiv:2604.06346v1 Announce Type: new Abstract: Large language models have shown strong potential for Arabic medical text generation; however, traditional fine-tuning objectives treat all medical cases uniformly, ignoring differences in clinical severity. This limitation is particularly critical in healthcare settings, where errors in severe cases contain higher clinical risk. In this work, we propose a severity-aware weighted loss for fine-tuning Arabic language models on medical complaint-response data. The method depends on soft severity probabilities to dynamically scale token-level loss contributions during optimization, thereby prioritizing clinically critical interactions without modifying model architectures. Experiments are conducted using the MAQA dataset, which provides Arabic medical complaints and trusted human responses. Severity labels and probabilistic scores are automatically derived using a fine-tuned AraBERT-based classifier and incorporated exclusively at the loss level. The proposed approach is evaluated across ten Arabic large language models of varying architectures and parameter scales. While standard cross-entropy fine-tuning yields only modest improvements, severity-aware optimization consistently achieves larger gains. Using a balanced weighting configuration, performance improves from 54.04% to 66.14% for AraGPT2-Base, from 59.16% to 67.18% for AraGPT2-Medium, and from 57.83% to 66.86% for Qwen2.5-0.5B, with peak performance reaching 67.18%. Overall, severity-aware fine-tuning delivers improvements of up to 12.10% over non-fine-tuned baselines, demonstrating robust and architecture-consistent gains.

Executive Summary

This article introduces a novel severity-aware weighted loss function for fine-tuning Arabic Large Language Models (LLMs) in medical text generation, addressing the critical limitation of uniform error treatment across varying clinical severities. By dynamically scaling token-level loss contributions based on soft severity probabilities, the method prioritizes clinically critical interactions without architectural modifications. Evaluated across ten Arabic LLMs using the MAQA dataset, the approach consistently demonstrates significant performance improvements (up to 12.10% over baselines) compared to standard cross-entropy fine-tuning, particularly for models like AraGPT2 and Qwen2.5. This research highlights a crucial advancement in enhancing the safety and reliability of AI in sensitive healthcare applications by embedding clinical risk awareness directly into the optimization process.

Key Points

  • ▸ Proposes a severity-aware weighted loss function for fine-tuning Arabic LLMs in medical text generation.
  • ▸ Dynamically scales token-level loss based on soft severity probabilities, prioritizing critical clinical cases.
  • ▸ Utilizes an AraBERT-based classifier to derive automatic severity labels and probabilistic scores for loss weighting.
  • ▸ Evaluated on the MAQA dataset across ten diverse Arabic LLMs, showing consistent and substantial performance gains.
  • ▸ Achieves up to 12.10% improvement over non-fine-tuned baselines, demonstrating the method's effectiveness and architectural consistency.

Merits

Addresses a Critical Gap

Successfully tackles the crucial issue of uniform error treatment in medical LLMs, which has significant clinical risk implications, moving towards more nuanced and responsible AI in healthcare.

Architectural Agnosticism

The proposed method integrates at the loss level, making it applicable across various LLM architectures without requiring modifications to the underlying model, enhancing its versatility and adoption potential.

Empirical Rigor

The extensive evaluation across ten different Arabic LLMs of varying scales and architectures, coupled with consistent performance improvements, lends strong credibility to the findings.

Automated Severity Labeling

Leveraging a fine-tuned AraBERT classifier for automatic severity scoring streamlines the process and reduces reliance on manual, potentially subjective, human annotation for weighting.

Demerits

Dependency on Severity Classifier Accuracy

The effectiveness of the weighted loss is inherently tied to the accuracy and reliability of the AraBERT-based severity classifier. Errors in severity prediction could propagate and misdirect the optimization.

Subjectivity of 'Severity'

While automated, the definition and probabilistic scoring of 'severity' can be inherently subjective and context-dependent in real-world clinical settings, potentially leading to biases or misinterpretations that the model then learns.

Lack of Qualitative Error Analysis

The abstract focuses heavily on quantitative performance gains. A qualitative analysis of the types of errors reduced in high-severity cases versus low-severity cases would provide deeper insights into the method's clinical utility.

Generalizability Beyond Arabic Medical Text

While highly promising for Arabic medical text, the direct transferability of the specific severity definitions and classifier to other languages or medical sub-domains requires further investigation.

Expert Commentary

This paper represents a significant conceptual and empirical leap in the application of Large Language Models within the highly sensitive domain of healthcare, particularly for Arabic medical contexts. The core innovation, a severity-aware weighted loss, moves beyond the simplistic 'all errors are equal' paradigm that has long plagued general-purpose AI applications in critical fields. By embedding clinical risk assessment directly into the optimization objective, the authors demonstrate a sophisticated understanding of real-world clinical priorities. The robust experimental validation across diverse LLM architectures underscores the method's generalizability and practical utility. However, the inherent subjectivity and potential biases in defining and automatically classifying 'severity' warrant deeper scrutiny. Future work should explore the interpretability of these severity scores and conduct comprehensive qualitative error analyses to truly ascertain the clinical impact. This research lays a crucial foundation for more responsible, safety-conscious AI in medicine, prompting a paradigm shift in how we design and evaluate LLMs for high-stakes applications.

Recommendations

  • ✓ Conduct a detailed qualitative error analysis, comparing the types of errors made by severity-aware models versus baseline models, specifically focusing on high-severity cases.
  • ✓ Investigate the robustness of the severity classifier to variations in input phrasing, slang, or incomplete information, and explore methods for uncertainty quantification in severity predictions.
  • ✓ Explore alternative or hybrid approaches for severity scoring, potentially incorporating expert clinical input or reinforcement learning from human feedback to refine the weighting mechanism.
  • ✓ Evaluate the method's performance on different Arabic medical datasets and potentially other languages/domains to assess its generalizability and identify any domain-specific adaptations required.
  • ✓ Provide insights into the computational overhead introduced by the severity classifier and the weighted loss mechanism during fine-tuning.

Sources

Original: arXiv - cs.CL

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