Why Trusting LLM Outputs in Production Can Be Misleading and How We Can Quantify It

Why Should We Care About LLM Uncertainty?

When deploying LLM based applications in production, especially in high-stakes domains, the challenge is not just about whether an answer is correct but whether the model knows that it is correct. Consider an LLM answering a structured query — “What is the recommended safety protocol for hazardous material transport?” — with the same confidence as a trivial factual response like “The capital of France is Paris.” If the model cannot differentiate between low-certainty and high-certainty predictions, it introduces a significant risk when deployed in decision-making systems.

This issue is central to the discussion in Rajamohan et al., 2025, where an ensemble-based approach is proposed to quantify uncertainty in LLM classifications. The core idea is that the variance in model outputs under controlled conditions provides a signal for conceptual certainty. This post breaks down that approach and its implications, particularly for multi-agent LLM-based architectures where uncertainty can accumulate across agents.

Photo by Phillip Glickman on Unsplash

How Can We Measure an LLM’s Confidence?

Uncertainty in LLM predictions arises from two major sources:

1. Conceptual Certainty — The internal parametric knowledge learned during training.
2. Input Variance — Sensitivity to different formulations of the same intent.

To quantify uncertainty effectively, both of these factors must be captured. The two primary classes of uncertainty estimation methods include:

1. White-Box Methods: Inspecting the Model’s Internal States

White-box methods leverage internal probability distributions within the model’s token generation process to estimate certainty. Examples include:

• Token Probability Metrics: Measures probability distributions over generated tokens.
• Mean Token Entropy: Quantifies variance across token probabilities.
• TokenSAR: A heuristic-based approach to assessing token-level relevance.

While these approaches provide fine-grained insights, they require direct access to model internals, which is infeasible for proprietary or API-based models.

2. Black-Box Methods: Measuring External Response Variability

Black-box methods estimate uncertainty by analyzing patterns in generated responses without accessing model internals. Notable techniques include:

• Lexical Similarity Analysis: Measuring divergence in multiple sampled outputs.
• Graph Laplacian Eigenvalues: Analyzing relationships between generated text variations.
• Uncertainty Tripartite Testing Paradigm (Unc-TTP): Evaluating consistency across repeated inferences.

While practical, black-box methods struggle to differentiate between true knowledge gaps and lexical sensitivity.

The Ensemble-Based Approach to Uncertainty Quantification

A more robust approach, detailed in the paper, is ensemble-based uncertainty measurement. Instead of relying on a single inference, this method generates multiple responses to the same underlying intent and evaluates the distribution of predictions.

Core Hypothesis

The model’s variance under a controlled inference setting provides a direct measure of conceptual certainty:

Variance in LLM Classification = F(Conceptual Certainty, Variance in Input)

This means that a well-trained model should yield stable classifications across phrasings. Conversely, large variances indicate low certainty.

How the Ensemble Method Works

The process involves three key steps:

1. Generate multiple phrasings of the same intent.
2. Run the LLM multiple times on each phrasing.
3. Analyze the distribution of predictions.

If a model frequently changes its response, uncertainty is high. If it remains stable, confidence is high.

Example Calculation

For 15 responses to a given query:

Predictions = {A, A, A, A, A, A, A, A, B, B, B, B, C, C, C}

• A = 8 votes, B = 4 votes, C = 3 votes.
• Prediction = A (highest votes).
• Ensemble Accuracy = 8/15 = 53.3%.

Why This Matters for Multi-Agent LLM Architectures

In multi-agent LLM systems, where different models collaborate in a pipeline (retrieval, synthesis, validation, summarization), uncertainty accumulates across agents. Consider a scenario where:

• The retrieval agent retrieves relevant information with 90% confidence.
• The reasoning agent processes the data with 85% certainty.
• The summarization agent generates an answer with 80% confidence.

By the time the response is delivered, true certainty is significantly lower than any individual agent’s confidence. This is why quantifying uncertainty at every stage is essential.

By applying ensemble-based uncertainty estimation at each step, we ensure:

✔️ Agents communicate confidence scores, enabling informed decision-making.

✔️ Compounded uncertainty is accounted for, reducing systemic overconfidence.

✔️ Models flag ambiguous responses, allowing for human oversight where necessary.

Wrapping up: Trust but Verify

Deploying LLMs in production environments demands more than just high accuracy — it requires a framework for quantifying uncertainty. The ensemble-based method described provides a practical and statistically sound approach to estimating certainty in model classifications.

This methodology shifts the focus from blindly trusting LLM outputs to a more rigorous approach where uncertainty is measured, controlled, and acted upon. As multi-agent LLM systems grow in complexity, building uncertainty-aware architectures will be critical to ensuring reliable AI-driven decision-making.