May 6, 2026

Can LLMs detect negation reliably in safety narratives? A practical look

There is a sentence pattern that appears in safety narratives all the time and that most pharmacovigilance NLP systems still struggle with:

The patient denied chest pain.

That sentence does not describe chest pain. It describes the absence of chest pain. But for an extraction system that is scanning for adverse events, the difference between “chest pain” and “no chest pain” is the entire difference between a true signal and a false one.

Negation has been a known hard problem in clinical and biomedical NLP for decades. The question now is whether large language models have actually solved it, or whether they have simply made it less visible while still failing in the cases that matter most.

Why negation matters so much in drug safety

In pharmacovigilance, the core task is often extraction: identify the drug, the adverse event, the patient, and the outcome from a source document. If an extraction system fails to recognise that an adverse event is negated, it will inject a false positive into the safety database. That false positive can distort disproportionality analyses, mislead case reviewers, and waste investigative effort.

The reverse is also a problem. If a model is overly cautious about negation and suppresses a genuinely reported event because nearby language looked negative, that is a missed signal.

What makes this especially tricky is that negation in clinical and safety text does not follow neat grammatical patterns. It shows up as explicit denial (“denies nausea”), hedged exclusion (“no evidence of hepatotoxicity at this time”), implied absence (“the rash resolved before treatment”), double negation (“not without risk”), and scope ambiguity (“no headache or dizziness was reported, but fatigue persisted”). Each of these requires a different kind of understanding.¹²

The rule-based era: NegEx and ConText

The earliest widely used approach to negation in clinical text was NegEx, developed by Chapman and colleagues. NegEx used a simple but effective strategy: scan for trigger terms like “no,” “denies,” or “was ruled out,” and apply them to nearby clinical concepts within a defined scope window.¹

ConText extended this approach to handle additional contextual features beyond negation, including whether a condition was historical, hypothetical, or experienced by someone other than the patient.²

These systems worked surprisingly well for common patterns. But they were brittle. Their performance depended heavily on trigger term lists, scope rules, and the assumption that negation in clinical text follows relatively predictable syntactic patterns. When it did not — when negation was implied rather than explicit, or when the scope crossed clause boundaries — they failed quietly.³

That fragility is a recurring theme. A system that works well on average but fails on the hard cases is particularly dangerous in pharmacovigilance, because the hard cases are often the ones that matter most for safety interpretation.

What transformer-based models changed

The shift to transformer-based architectures, and especially pre-trained language models like BERT and its biomedical variants, brought a genuine improvement. These models can learn negation patterns from data rather than relying on hand-coded rules. They can, in principle, handle long-range dependencies, implicit negation, and scope ambiguity better than rule-based systems.⁴

In practice, the improvement is real but uneven. Fine-tuned transformer models have shown strong performance on standard assertion detection benchmarks, outperforming both legacy rule-based approaches and general-purpose commercial APIs. One recent study found that fine-tuned models significantly outperformed cloud-based solutions like AWS Medical Comprehend, Azure AI Text Analytics, and GPT-4o on assertion detection tasks in clinical text.⁴

But here is the important caveat: those benchmarks tend to overrepresent common negation patterns. They may not fully capture the tail distribution of unusual, ambiguous, or domain-specific negation that appears in pharmacovigilance narratives.

LLMs and the negation problem: better, but not solved

Large language models like GPT-4 bring even more contextual understanding to the table. They can handle many negation patterns that would trip up older systems, including double negation, hedged language, and complex scope.

But the evidence suggests they are far from reliable on the cases that matter most. Work on adverse event extraction from structured product labels has shown that LLM performance varies significantly depending on the section of the label, the linguistic complexity of the target concept, and whether the term is negated or discontinuous. Negated and discontinuous terms were substantially harder to extract correctly.⁵

Similarly, research specifically focused on negation robustness in adverse drug event detection has found that state-of-the-art extraction models remain fragile when exposed to negated samples. One study introduced the SNAX benchmark to test ADE detection systems against negated and speculated adverse events, and found that models produced a high number of spurious entities. Targeted strategies to improve robustness reduced these false positives by 60% for negation and 80% for speculation, but the baseline fragility was striking.⁶

An earlier related benchmark, NADE, showed a similar pattern: ADE detection systems that performed well on standard datasets saw significant degradation when tested on samples containing negated adverse events.⁷

These findings are important because they suggest that the negation problem in pharmacovigilance NLP is not just a legacy issue from the rule-based era. It persists in modern systems, including large language models, and it persists precisely in the linguistically complex cases where safety interpretation depends on getting the answer right.

The generalisation gap

There is also a deeper methodological concern. A well-known study by Wu and colleagues argued that an optimisable solution to negation detection is not the same as a generalisable one. They showed that negation detection performance dropped substantially when models trained on one clinical domain were applied to another, even when both domains used similar clinical language.³

That finding has direct implications for pharmacovigilance. Safety narratives come from many sources: case reports, clinical notes, product labels, literature, patient submissions, and regulatory correspondence. Each source has its own linguistic conventions. A negation detection system trained on discharge summaries may not transfer well to ICSR narratives, and a system tuned for structured product labels may struggle with the more conversational language of consumer reports.

This means that even if a model performs well on a published benchmark, its real-world reliability in a pharmacovigilance pipeline depends on how well its training data matches the specific source material it will encounter.

What good practice looks like

Given all of this, what should teams building NLP systems for drug safety actually do about negation?

First, test explicitly for negation robustness. Standard extraction benchmarks are not enough. Systems should be evaluated on datasets that include a meaningful proportion of negated, speculated, and hedged adverse event mentions. If a system has not been stress-tested against negation, its overall performance numbers are misleading.

Second, treat negation as an assertion classification problem, not just a binary toggle. The question is not only “is this concept negated?” but also “is it hypothetical, historical, uncertain, or attributed to someone other than the patient?” That richer frame, which was already part of the ConText design, is even more important in an LLM context where the model can handle these distinctions if properly prompted or fine-tuned.²⁴

Third, build in traceability. When a model classifies an adverse event as present or absent, the system should be able to show the reviewer exactly which text triggered that classification. In a regulated environment, a correct answer that cannot be explained is almost as problematic as a wrong one.

And fourth, accept that negation is not solved. It is mitigated, sometimes impressively, but every new source type, every new linguistic pattern, and every new deployment context can reintroduce the problem. Continuous evaluation is not optional.

The honest summary

LLMs are better at negation than rule-based systems. Fine-tuned models are better than general-purpose ones. But “better” is not “reliable,” and the gap between average performance and worst-case performance is exactly where pharmacovigilance cannot afford to be careless.

The real enemy, as one study of label extraction put it, is not low average performance. It is fragile performance in the difficult linguistic cases that matter most for safety interpretation.⁵ Negation is one of the clearest examples of that fragility.

1. Chapman WW, Bridewell W, Hanbury P, Cooper GF, Buchanan BG. A simple algorithm for identifying negated findings and diseases in discharge summaries. Journal of Biomedical Informatics. 2001;34(5):301–310. ↩ ↩²

2. Harkema H, Dowling JN, Thornblade T, Chapman WW. ConText: An algorithm for determining negation, experiencer, and temporal status from clinical reports. Journal of Biomedical Informatics. 2009;42(5):839–851. ↩ ↩² ↩³

3. Wu S, Miller T, Masanz J, et al. Negation’s not solved: generalizability versus optimizability in clinical natural language processing. PLoS ONE. 2014;9(11):e112774. ↩ ↩²

4. Kocaman V, Gul Y, Kaya MA, et al. Beyond negation detection: comprehensive assertion detection models for clinical NLP. Proceedings of Text2Story’25 Workshop. 2025. ↩ ↩² ↩³

5. Gisladottir U, Zietz M, Kivelson S, et al. Leveraging large language models in extracting drug safety information from prescription drug labels. Drug Safety. 2025. doi:10.1007/s40264-025-01594-x ↩ ↩²

6. Scaboro S, Portelli B, Chersoni E, Serra G, Ferraro G. Increasing adverse drug events extraction robustness on social media: case study on negation and speculation. Journal of Biomedical Informatics. 2023;137:104275. ↩

7. Portelli B, Scaboro S, Ferraro G, Serra G, Chersoni E. NADE: A benchmark for robust adverse drug events extraction in face of negations. Proceedings of the Seventh Workshop on Noisy User-generated Text (W-NUT). 2021:42–50. ↩

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