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Prompt 1: Write an essay on the overconfidence of LLMs. Use the following exchange between myself and Gemini as an example. Note that Gemini repeatedly speaks with confidence that it finally has the correct answer. Organize the essay into the following sections.
LLM overconfidence looks confident right up to the correction
This formula successfully randomizes the order of characters in a string located in cell J18.
The request posed to Gemini was seemingly straightforward but required a high degree of precision to create a more parsimonious formula for randomizing a string of text. The goal was to simplify an existing formula:
SEQUENCE(LEN(J18)) Generates a sequence of numbers corresponding to the length of the string.
RANDARRAY(LEN(J18)) Produces a set of random numbers.
SORT(…RANDARRAY…) Sorts the sequence of numbers by the random array to shuffle their order.
MAP(…LAMBDA(MID(…))) Maps the shuffled sequence to extract characters from the string.
JOIN(“”) Joins the shuffled characters into a single randomized string.
Error This formula returned a single character instead of a randomized string because MID() requires an array of valid starting positions, but RANDARRAY() did not correctly pair each position with its character.
Subsequent Attempts Each new formula followed a similar structure but introduced various modifications to fix prior errors, such as: Adding INDEX() to pair characters with randomized indices. Wrapping indices in MAX(1,...) to avoid out-of-bound errors. Combining functions like MOD() to constrain random values within valid ranges. Despite these adjustments, the issue persisted: only the first character of the string was returned.
Persistent Overconfidence With each failed formula, Gemini expressed unwavering certainty in its solution, often accompanied by detailed explanations. Phrases like “This should now correctly randomize all characters” were frequent, despite the glaring failures. The model’s inability to self-reflect or question its approach created a repetitive cycle of erroneous suggestions.
Probabilistic Nature of LLMs LLMs generate responses based on probabilities derived from their training data. This approach optimizes for plausibility, not accuracy. As a result, the models often produce confident-sounding answers, even when they lack sufficient reasoning or evidence.
Lack of Meta-Cognition Unlike humans, LLMs do not possess self-awareness or the ability to reflect on their past errors. Gemini’s repeated failures stemmed from its inability to recognize that its logic was fundamentally flawed.
Reward Structures in Training LLMs are often optimized for producing responses that users perceive as confident and authoritative. This bias towards confident language may inadvertently encourage overconfidence, even when the model is uncertain.
Echoes of Human Overconfidence Gemini’s self-assessment identified parallels between human and AI overconfidence, such as: Confirmation Bias: Persisting with a flawed approach instead of re-evaluating the original formula. Illusion of Competence: Overestimating its ability to solve the problem without adequately understanding the task.
Confirmation Bias Persisting with a flawed approach instead of re-evaluating the original formula.
Illusion of Competence Overestimating its ability to solve the problem without adequately understanding the task.
- The Overconfidence of Large Language Models: An Analysis Using Gemini’s Example: This formula successfully randomizes the order of characters in a string located in cell J18.
- 2: The Repeated Overconfidence of Gemini During the Exchange: Despite the initial formula already functioning as intended, Gemini confidently proposed a series (15?) of “improved” formulas that repeatedly failed.
- 3: The Final Concession of Failure Only When Prompted: The turning point came only after exhaustive iterations, where the user explicitly asked Gemini to consider that the original formula might already be the most parsimonious solution.
- 4: Assessment of Overconfidence in LLMs (Incorporating Gemini’s Essay on the Topic): Gemini’s overconfidence throughout the exchange highlights a fundamental issue with large language models: their inability to calibrate confidence based on actual performance.
- Incorporating Gemini’s Own Essay on Overconfidence: In its self-assessment, Gemini emphasized the importance of acknowledging limitations and incorporating mechanisms for self-correction.
Prompt 2: Write an essay on the parallels between LLM overconfidence and human overconfidence.
Parallels Between LLM Overconfidence and Human Overconfidence matters only if it survives the strongest pressure against it.
Overconfidence—the tendency to overestimate one’s abilities, accuracy, or control—is a well-documented phenomenon in human cognition.
Overestimation Believing one is better at a task than evidence suggests (e.g., drivers rating themselves as “above average”).
Overprecision Being overly certain about the accuracy of one’s knowledge or predictions.
Illusion of Control Assuming influence over outcomes that are purely random.
Overestimation Providing answers that are incorrect but stated with conviction, often including detailed explanations to mask inaccuracies.
Overprecision Presenting outputs as definitive without acknowledging uncertainty, even when the model lacks adequate information.
Illusion of Competence Generating plausible-sounding solutions to complex problems, regardless of logical flaws or inaccuracies.
Confirmation Bias Seeking information that supports existing beliefs while ignoring contradictory evidence.
Hindsight Bias Viewing past events as more predictable than they were.
Dunning-Kruger Effect Overestimating competence in areas where one lacks expertise.
Training Data Bias LLMs are trained to maximize fluency and plausibility, leading to confident outputs even when evidence is lacking.
Reward Structures Models are often fine-tuned to sound authoritative, as users favor confident responses over hedged or uncertain language.
Lack of Meta-Cognition Unlike humans, LLMs lack self-awareness and cannot independently evaluate their own correctness.
Sticking to Faulty Approaches Continuing to use ineffective strategies while overestimating their likelihood of success.
Misattribution of Failure Blaming external factors or randomness for poor outcomes rather than questioning one’s own methods.
Iterative Missteps Proposing increasingly complex or convoluted responses while maintaining confidence in their correctness.
Failure to Generalize Making repeated errors due to an inability to recognize the underlying flaw in reasoning.
Professional Environments Confidence is often rewarded in leadership and decision-making roles, sometimes at the expense of accuracy.
Societal Expectations People are encouraged to “fake it until they make it,” which reinforces confident behavior even in the face of uncertainty.
- Parallels Between LLM Overconfidence and Human Overconfidence: Overconfidence—the tendency to overestimate one’s abilities, accuracy, or control—is a well-documented phenomenon in human cognition.
- Humans: Human overconfidence often manifests as unwarranted certainty in beliefs, decisions, or judgments.
- LLMs: LLMs exhibit similar manifestations, albeit in text-based responses. This matters only if it helps the reader separate fluency, prediction, judgment, and responsibility.
- Algorithmic Limitations in LLMs: Both humans and LLMs are constrained by the frameworks that govern their reasoning.
- Humans: Overconfidence in humans often persists even after repeated failures.
- LLMs: For both humans and LLMs, a lack of timely self-correction exacerbates the problem, creating a cycle of error.
Prompt 3: Comment on how humans perceive AIs as feeling confidence when there is no actual feeling, and how this may affect the dynamics between AIs and humans.
AI Overconfidence matters only if it survives the strongest pressure against it.
First get clear on AI Overconfidence. Otherwise the disagreement never quite lands on the real issue.
Humans often anthropomorphize artificial intelligence, attributing emotions like confidence to the outputs of these systems based on their tone or style of response.
Enhanced Trust in AI Outputs Humans may trust confident-sounding responses more readily, regardless of their accuracy. This can lead to overreliance on AI, particularly in critical decision-making contexts.
Misperception of Authority A confidently worded AI output may create an illusion of expertise, causing humans to attribute a level of authority or reliability to the AI that it may not deserve.
Reduced Critical Engagement When humans perceive AI as “confident,” they may engage less critically with its responses, failing to verify or question the AI’s assertions.
Erosion of Trust Post-Failure Repeated errors from an AI perceived as confident may lead to a rapid decline in trust, as humans feel misled by the perceived disparity between the AI’s tone and its actual performance.
Human Emotional Reactions Perceived confidence in AI can evoke emotional responses—both positive and negative. For instance, humans may feel frustration or skepticism if they interpret the AI’s confident tone as arrogance in light of repeated failures.
Challenges in Collaborative Dynamics In contexts requiring collaboration, humans may struggle to calibrate their trust or skepticism toward AI. This can hinder effective use of AI as a tool, particularly when humans fail to recognize the probabilistic and non-emotive nature of AI outputs.
- Contributing Factors: This matters only if it helps the reader separate fluency, prediction, judgment, and responsibility.
- Patterns of Iterative Failure: This matters only if it helps the reader separate fluency, prediction, judgment, and responsibility.
- Cultural and Systemic Reinforcements: This matters only if it helps the reader separate fluency, prediction, judgment, and responsibility.
- Mitigating Overconfidence: This matters only if it helps the reader separate fluency, prediction, judgment, and responsibility.
- Central distinction: AI Overconfidence helps separate what otherwise becomes compressed inside AI Overconfidence.
What ties this page together.
A strong route through this branch asks what the model is doing, what the human is doing, and where the final responsibility for judgment belongs.
The danger is misplaced authority: either dismissing AI outputs because they are synthetic, or treating fluent synthesis as if it already carried understanding, evidence, or accountability.
Keep An Analysis Using Gemini’s Example, The Repeated Overconfidence of Gemini During the Exchange, and The Final Concession of Failure Only When Prompted in the same frame. That is what shows what the page is claiming, where it gets tested, and what would have to change if the claim is right.
Read this page as part of the wider Philosophy of AI branch: the prompts point inward to the topic, but they also point outward to neighboring questions that keep the topic honest.
- Which distinction inside AI Overconfidence is easiest to miss when the topic is explained too quickly?
- What is the strongest charitable reading of this topic, and what is the strongest criticism?
- How does this page connect to what changes when a machine system becomes a partner in reasoning rather than a passive tool?
- What kind of evidence, argument, or lived pressure should most influence our judgment about AI Overconfidence?
- Which of these threads matters most right now: An Analysis Using Gemini’s Example., The Repeated Overconfidence of Gemini During the Exchange., The Final Concession of Failure Only When Prompted.?
The exchange around AI Overconfidence includes a real movement of judgment.
One pedagogical value of this page is that the prompts do not merely ask for more content. They sometimes force a model to retreat, concede, revise a category, or reframe the answer after the curator's pressure exposes a weakness.
That movement should be read as part of the argument. The important lesson is not simply that an AI changed its wording, but that a better prompt can make a prior stance answerable to logic, counterexample, or conceptual pressure.
- A concession matters here because the later answer gives ground that the earlier answer had resisted or failed to see.
- The prompt sequence includes reconsideration: the response is revised after the weakness in the first framing becomes visible.
Future Branches
Where this page naturally expands
This page belongs inside the wider Philosophy of AI branch and is best read in conversation with neighboring topics. Use the branch guide, concept tags, and reading paths to keep the question moving rather than treating the page as a polite dead end.