Using LLM for confidence scoring is a bad idea
Source Entity
Hacker News
Relying on Large Language Models to generate confidence scores is fundamentally flawed due to their probabilistic nature. These models lack true self-awareness and calibration, leading to unreliable metrics that should not guide critical decision-making processes.
The Fallacy of LLM-Generated Confidence Scores
In the rapidly evolving landscape of artificial intelligence, Large Language Models (LLMs) have demonstrated remarkable utility in coding, summarization, and task orchestration. However, a growing trend among agent designers is the practice of tasking these models with generating numerical confidence scores to gauge the reliability of their own outputs. This practice is fundamentally misguided, as it conflates the model's ability to generate text with an internal mechanism for probabilistic self-assessment.
The Misunderstanding of Confidence
To understand why LLMs fail at self-assessment, one must first define what a confidence score represents. In traditional statistical modeling, a confidence score—like a meteorologist predicting a 70% chance of rain—is grounded in historical data and frequentist probability. It reflects a calibrated assessment of how often a specific outcome occurs under similar conditions. LLMs, conversely, do not possess this grounded understanding; they are predictive text engines that generate tokens based on statistical likelihoods within a latent space, not empirical reality.
The Calibration Gap
LLMs operate by predicting the next token in a sequence, effectively optimizing for coherence rather than truth. When asked for a confidence score, the model is merely completing a pattern based on its training data. If the model has been exposed to datasets where humans provide confidence intervals, it mimics that behavior without the underlying logic. This results in a lack of calibration—a state where the model's stated confidence does not correlate with its actual accuracy, making the score effectively meaningless.
Risks in Agentic Architectures
Many modern agentic frameworks rely on these scores to determine if an action should be executed or if a human should intervene. By trusting an uncalibrated LLM to report its own uncertainty, designers risk creating brittle systems that are prone to 'hallucinations' or high-confidence errors. When a system cannot accurately evaluate its own limitations, it becomes a liability in high-stakes environments such as legal document review, medical diagnostics, or financial planning.
Future Directions in Uncertainty Estimation
Moving forward, the industry must move away from querying LLMs for subjective confidence and toward more rigorous methods of uncertainty quantification. Techniques such as Bayesian neural networks, ensemble methods, or external verification loops provide more robust ways to measure reliability. Relying on the model's internal 'opinion' of its performance is a shortcut that sidesteps the necessary engineering rigor required for reliable AI deployment.
Conclusion
Ultimately, the quest for a 'confidence score' from an LLM is a categorical error. While these models are powerful tools for generation and transformation, they are not self-aware entities capable of introspection. Developers must treat LLM outputs as probabilistic suggestions rather than verified truths, ensuring that critical decision-making remains decoupled from the model's own synthetic confidence.