LLMs: Sometimes Confident, Sometimes Clueless, Just Like Us (But Less Likely to Learn From Mistakes)

Overview

Paper Summary › Explain Like I'm Five › Conflicts of Interest › Identified Limitations › Rating Explanation › Good to know › Topic Hierarchy › File Information ›

Paper Summary

Paperzilla title

Across five different tasks, LLMs demonstrated mixed metacognitive accuracy in their confidence judgments, sometimes outperforming and sometimes underperforming humans, but generally performing slightly *better*. A key finding is that several LLMs were less likely than humans to improve their metacognitive calibration after completing a task, suggesting a limitation in learning from experience. Overall, LLM confidence isn't uniformly better or worse than human confidence, varying considerably by the specific model and task.

Explain Like I'm Five

Scientists found that computers are sometimes good at knowing how sure they are about an answer, and sometimes not, but generally a little better than people. However, they don't learn from mistakes to get better at being sure like humans do.

Possible Conflicts of Interest

The authors acknowledge using LLMs for proofreading, but declare no other conflicts of interest. Funding was provided by the National Science Foundation and Carnegie Mellon University.

Identified Limitations

Small Sample Sizes for LLM Analyses

The sample sizes for the LLM analyses were often quite small, particularly in Study 2, which limits the reliability and generalizability of the findings. Larger sample sizes would provide more robust estimates of the LLMs' metacognitive accuracy.

Comparison of Average Humans to Best LLMs

The study primarily focuses on comparing average humans to the *best* LLMs. It does not examine individual differences in metacognitive abilities amongst humans (e.g., experts vs. novices) or amongst LLMs. A comparison of the *best* humans to the *best* LLMs might yield different results.

Limited range of Domains and Tasks

The study relies on a limited set of domains and tasks to assess metacognitive accuracy. The findings may not generalize to other tasks or domains where uncertainty plays a different role or where LLMs might have different strengths and weaknesses. Testing metacognition in different contexts can provide more diverse insights.

Confound Between Uncertainty Type and Study Timing

The study acknowledges a potential confound between aleatory/epistemic uncertainty and study timing, but doesn't fully address it. If earlier studies focused on aleatory uncertainty and later ones on epistemic uncertainty, this time-based difference could be mistaken for differences in how LLMs handle these uncertainty types. It makes it hard to separate the effect of time/LLM development from the effect of uncertainty type.

Rating Explanation

This paper explores a timely and relevant topic—the metacognitive abilities of LLMs—using a rigorous experimental approach. Comparing LLMs directly to humans across multiple studies is a strength. While limitations regarding sample size, task variety, and potential confounds exist, the overall methodology is solid and the findings provide valuable insights. The paper is well-written and presents a balanced perspective on LLM capabilities.

Good to know

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Topic Hierarchy

Domain: Social Sciences

Field: Psychology

Subfield: Experimental and Cognitive Psychology

File Information

Original Title: Quantifying uncert-Al-nty: Testing the accuracy of LLMs' confidence judgments

Uploaded: July 22, 2025 at 06:21 PM

Privacy: Public