Enterprise AI Analysis
When Stability Fails: Hidden Failure Modes of LLMs in Data-Constrained Scientific Decision-Making
This paper investigates the reliability of Large Language Models (LLMs) as decision-support tools in data-constrained scientific workflows, particularly in gene prioritization. It introduces a behavioral evaluation framework assessing stability, correctness against statistical ground truth, prompt sensitivity, and output validity. Findings reveal that LLMs can exhibit high run-to-run stability while systematically diverging from statistical ground truth, being highly sensitive to minor prompt wording changes, and generating invalid outputs. This highlights that stability alone is insufficient for reliable scientific decision-making, emphasizing the need for explicit ground-truth validation and output validity checks.
Key Findings at a Glance
LLM Stability (Jaccard)
Agreement with Ground Truth
Prompt Sensitivity (Jaccard)
Deep Analysis & Enterprise Applications
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Scientific Analysis
Failure Modes
Evaluation Framework
LLM Reliability in Scientific Contexts
In data-constrained scientific workflows, LLM reliability is paramount. This research dissects several dimensions: stability, correctness, prompt sensitivity, and output validity. The results underscore that merely achieving high stability in repeated runs does not equate to correctness when a statistical ground truth exists.
Diagnosing LLM Failure Modes
This study reveals four critical failure modes: systematic divergence from ground truth despite high stability, extreme prompt sensitivity to minor wording changes, over-selection under relaxed thresholds, and the hallucination of syntactically plausible but invalid data. These issues are particularly problematic in high-stakes scientific decision-making.
A New Evaluation Paradigm
The proposed behavioral evaluation framework explicitly separates the dimensions of stability, correctness, prompt sensitivity, and output validity. This comprehensive approach allows for a controlled diagnosis of LLM behavior, moving beyond simple reproducibility metrics to assess true scientific utility.
74%
Prompt Wording Sensitivity (Jaccard drop for ChatGPT P7a vs P7b)
Enterprise Process Flow
Fixed DE Table Input
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LLM Query (Repeated Runs)
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Output Parsing
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Compare to DESeq2 Reference
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Assess Stability
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Assess Correctness
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Assess Sensitivity
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Assess Validity
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Identify Failure Modes
| Model | Run-to-Run Stability (Jaccard) | Agreement with Ground Truth (Jaccard) | Hallucinated IDs per run |
|---|---|---|---|
| ChatGPT | 1.00 | 1.00 | 0 |
| Gemini | 1.00 | 1.00 | 0 |
| Claude | 1.00 | 0.00 | 20 |
Case Study: Borderline Ranking Uncertainty (P6)
In the borderline Top-20 ranking scenario (P6), models were tasked with prioritizing genes under uncertainty. Gemini achieved perfect agreement (Jaccard = 1.00) with the deterministic reference, demonstrating its ability to handle nuanced statistical ranking. In contrast, ChatGPT diverged significantly (Jaccard = 0.14), and Claude again returned no true signal. This highlights varying capabilities in handling complex, data-constrained decision rules.
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Your Enterprise AI Roadmap
A structured approach to leveraging LLMs responsibly in your scientific workflows.
Phase 1: Controlled Experiment Design
Define statistical gene prioritization task, fix differential expression table, and design prompt regimes varying thresholds and wording. Ensure deterministic ground truth reference.
Phase 2: LLM Query Execution & Data Collection
Query selected LLMs (ChatGPT, Gemini, Claude) multiple times per prompt regime with identical inputs. Collect raw outputs for analysis.
Phase 3: Output Parsing & Metric Calculation
Develop automated parsing for gene sets and compute evaluation metrics: Jaccard for stability/correctness, overlap coefficient for containment, and validity checks for hallucinated IDs.
Phase 4: Behavioral Analysis & Failure Mode Identification
Analyze LLM outputs across the four dimensions (stability, correctness, sensitivity, validity) to identify and categorize specific failure modes, such as systematic divergence or prompt sensitivity.
Phase 5: Reporting & Recommendations
Summarize findings, quantify performance, and formulate recommendations for responsible LLM deployment in data-constrained scientific workflows, emphasizing explicit validation.
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