Evaluating LLM performance on fundamental microbial phenotype prediction

P. C. Münch, N. Safaei, R. Mreches, M. Binder, Y. Han, G. Robertson, E. A. Franzosa, C. Huttenhower, A. C. McHardy

Comprehensive benchmarking of language models predicting microbial characteristics

Understanding Phenotype Predictions

Large language models have shown remarkable capabilities in understanding and generating scientific text, but their performance on specialized microbiology tasks remains largely unexplored. This analysis evaluates how different models predict fundamental bacterial characteristics—from gram staining to pathogenicity—revealing distinct patterns in their biological knowledge representation and confidence levels.

Microbial phenotypes represent fundamental characteristics that define how bacteria interact with their environment. These traits range from structural properties like cell wall composition to functional capabilities such as oxygen requirements and pathogenicity. Understanding how well language models predict these characteristics provides insights into their biological knowledge representation.

Clinical Relevance
Accurate phenotype predictions are crucial for clinical microbiology, where rapid identification of bacterial characteristics can inform treatment decisions. Models that excel at predicting pathogenicity and antibiotic resistance markers could serve as valuable preliminary screening tools. Understanding how language models predict bacterial phenotypes has significant implications for computational microbiology and bioinformatics research, accelerating initial bacterial characterization, guiding experimental design, and providing insights into knowledge gaps in current biological databases. Future research directions include fine-tuning models on curated microbiological datasets, developing ensemble approaches that combine multiple model predictions, and creating specialized benchmarks for evaluating biological prediction accuracy across diverse bacterial taxa.

The clinical and research scenarios above underscore why rapid phenotype assessment matters, but they only hint at the breadth of information microbiologists need from language models. Before we compare prediction accuracy, we orient the reader around the phenotype families that appear most often in pathogen characterization and surveillance workflows.

The next section introduces those core phenotype categories—ranging from Gram staining and motility to virulence traits and biosafety considerations—so that the downstream analyses can be interpreted in the context of the decisions practitioners make every day.

Key Phenotype Categories

Our analysis evaluates model predictions across essential microbial characteristics that form the foundation of bacterial classification and identification in clinical and research settings.

The ability to accurately predict bacterial phenotypes from species names represents a critical intersection of computational biology and practical microbiology. In clinical settings, rapid phenotype prediction can mean the difference between timely, targeted treatment and delayed intervention. When a clinician encounters an unfamiliar bacterial species, immediate access to predicted characteristics—such as Gram staining properties, oxygen requirements, or potential pathogenicity—can guide initial diagnostic and therapeutic decisions while awaiting culture results. Beyond immediate clinical applications, phenotype predictions serve as powerful tools for biological discovery, helping bridge the gap between rapid genomic identification and traditional laboratory characterization. These predictions can prioritize experimental validation efforts, focusing limited laboratory resources on the most promising or clinically relevant discoveries.

Ground Truth Snapshot (Cached)

Precomputed phenotype statistics served instantly from cache, with manual refresh to recalculate after dataset updates.

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Select a ground truth dataset to view cached phenotype statistics

Analysis Insights

The ground truth dataset statistics reveal several important patterns in phenotype data availability. While some characteristics like gram staining and cell shape have extensive coverage across bacterial species, others such as hemolysis and biosafety level show more limited annotation availability. This variance in data completeness presents both challenges and opportunities for language model evaluation.

Interestingly, the distribution of phenotype values within each characteristic often reflects biological reality. For instance, the predominance of non-pathogenic organisms in our dataset mirrors the fact that most bacterial species are not human pathogens. Similarly, the high proportion of species without extreme environment tolerance aligns with the ecological distribution of bacteria in nature.

These patterns become particularly relevant when evaluating model predictions. Models that default to the most common values may achieve reasonable accuracy on imbalanced datasets, but this approach fails to capture the nuanced understanding required for less common phenotypes. Our analysis methodology specifically addresses this challenge through balanced accuracy metrics and stratified evaluation approaches.

Phenotype Query Templates

Explore the structured prompts used to query language models for phenotype predictions, including system prompts, user queries, and validation schemas.

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Species Popularity Analysis

Interactive visualization of search frequency distribution across bacterial species.

Understanding aggregate search demand is only the first step. Once we know which organisms attract the most attention, the next question is who is asking and how that interest varies across expertise levels.

The following analysis segments search activity by respondent knowledge, letting us see whether clinicians, researchers, or public users focus on the same species. Those patterns help prioritize documentation, model fine-tuning, and outreach for the communities that need accurate phenotype answers the most.

Search Frequency by Knowledge Group

Distribution of Google Scholar search counts across model-reported knowledge levels, revealing how research attention correlates with model confidence.

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The economic impact of accurate phenotype prediction extends beyond individual patient care. Healthcare systems worldwide face increasing pressure from antimicrobial resistance and emerging pathogens. By rapidly identifying bacterial characteristics that influence treatment efficacy—such as biofilm formation capability or inherent antibiotic resistance patterns—predictive models can help optimize antibiotic stewardship programs and reduce the development of resistant strains. This proactive approach to bacterial characterization represents a crucial tool in our ongoing battle against antimicrobial resistance.

Analysis Methodology

We queried multiple state-of-the-art language models with a comprehensive list of bacterial species names, asking them to predict phenotypic characteristics. The models were evaluated on their ability to provide specific predictions versus acknowledging uncertainty with "NA" responses.

The distribution patterns help identify which models are more likely to provide actionable predictions for microbiological research and which tend to be more cautious in their assessments. This balance between specificity and uncertainty is crucial for practical applications in microbiology.

Model Accuracy Snapshot (Cached)

Instant view of precomputed accuracy metrics. Refresh the snapshot after updating ground truth datasets or importing new model predictions.

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Select a ground truth dataset to load cached model accuracy metrics

The cached accuracy snapshot provides a fast baseline for how each language model performs when asked to recover microbial phenotypes. With those reference points in mind, the next visualization explores how balanced accuracy shifts across publication years, highlighting both the pace of improvement in newer releases and the veteran models that still compete in high-signal phenotype categories.

Model Performance vs. Publication Year

Cached analysis of how model performance correlates with publication date, using metadata from recent model releases to identify trends in phenotype prediction accuracy over time.

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Models with Metadata

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Date Range

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Publication coverage

Performance Correlation

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Performance vs. Year

Top Recent Model

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Best performer in last 6 months

Publication timelines reveal which releases set the pace for phenotype accuracy. To understand how architectural scale contributes to those jumps, we next compare parameter counts against the same benchmark signals.

Model Size vs. Performance

Exploring the relationship between model parameters and phenotype prediction accuracy, revealing efficiency trends and the performance-size trade-off in biological knowledge tasks.

Models with Size Data

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Size vs. Performance

Most Efficient Model

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Best performance per parameter

Scale is only part of the story—some compact models punch above their weight while larger systems plateau. The following snapshot distills those dynamics into quick performance ranges so you can see where each model reliably operates.

Model Performance Ranges (Cached)

Precomputed accuracy ranges loaded instantly. Refresh the snapshot after updating ground truth datasets or predictions.

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Select a dataset above to view model performance ranges

With the range of outcomes in mind, we turn to an aggregate leaderboard. The ranking view highlights which models consistently rise to the top once their scores are normalized across phenotypes and datasets.

Overall Performance Ranking (Cached)

Instant overview of precomputed balanced accuracy and precision rankings. Refresh the snapshot after dataset or prediction updates.

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Overall LLM Performance

Average performance across all phenotypes

Balanced Accuracy

Precision

Select a dataset to view overall model performance rankings

Overall position is helpful, but most teams care about phenotype-specific wins. The next view breaks down which model leads for each trait, making it easier to spot complementary strengths.

Best Models Per Phenotype (Cached)

Instant access to the top-performing model per phenotype using the shared accuracy snapshot. Refresh after updates to ground truth or predictions.

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Top Performing Models per Phenotype

Sorted by selected metric (balanced accuracy by default). Toggle to view precision leaders.

Phenotype Best Model Performance Sample Size

Select a dataset to see the top model per phenotype

Phenotype-level champions also reveal where models underperform. To understand how knowledge availability shapes those gaps, we pivot to accuracy segmented by literature coverage.

Accuracy by Knowledge Group (Cached)

Instant knowledge-stratified accuracy view derived from the shared cache. Refresh after updating ground truth datasets or predictions.

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Select a ground truth dataset to analyze model accuracy by knowledge group

Knowledge stratification shows where training data density matters most. The upcoming visualization overlays those same groups with interactive controls so you can explore how sampling choices reshape the picture.

Phenotype Accuracy Across Knowledge Groups (Cached)

Fast-loading snapshot of cached knowledge-stratified phenotype accuracy with dataset, model, and sampling controls. Refresh after updating ground truth or model predictions.

Select a dataset to load cached metrics

Select a dataset to load cached knowledge accuracy metrics.

The interactive controls help isolate individual sampling strategies, but trends over time surface longer arcs. We therefore close the accuracy suite with cached trajectories that track improvement waves across releases.

Knowledge Accuracy Trends (Cached)

Precomputed knowledge-trend visualization with cached weighted accuracy metrics and instant toggles. Refresh after updating ground truth or cached snapshots.

Accuracy trajectories set expectations for future releases, but they do not show how models expand the underlying dataset. The next section quantifies those additions so you can weigh coverage gains alongside correctness.