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AI Overconfidence

  • “Human overconfidence often manifests as unwarranted certainty in beliefs, decisions, or judgments.”

  • “LLMs exhibit similar manifestations, providing answers that are incorrect but stated with conviction.”

  • “Both humans and LLMs are constrained by the frameworks that govern their reasoning.”

  • “Overconfidence in humans often persists even after repeated failures, sticking to faulty approaches while overestimating success.”

  • “LLMs can persist with incorrect solutions, proposing increasingly convoluted responses while maintaining confidence.”

  • “Social and cultural systems incentivize overconfidence, rewarding confidence over accuracy.”

  • “Addressing overconfidence in both humans and LLMs requires deliberate interventions to emphasize humility and accountability.”

Table of Contents: (Click any link below to navigate to that section.)

  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.
    1. The Overconfidence of Large Language Models: An Analysis Using Gemini’s Example
      1. 1: What Was the Request?
      2. 2: The Repeated Overconfidence of Gemini During the Exchange
      3. 3: The Final Concession of Failure Only When Prompted
      4. 4: Assessment of Overconfidence in LLMs (Incorporating Gemini’s Essay on the Topic)
      5. Incorporating Gemini’s Own Essay on Overconfidence
    2. Conclusion
  2. Write an essay on the parallels between LLM overconfidence and human overconfidence.
    1. Parallels Between LLM Overconfidence and Human Overconfidence
      1. 1. Manifestation of Overconfidence
        1. Humans:
        2. LLMs:
      2. 2. Contributing Factors
        1. Cognitive Biases in Humans:
        2. Algorithmic Limitations in LLMs:
      3. 3. Patterns of Iterative Failure
        1. Humans:
        2. LLMs:
      4. 4. Cultural and Systemic Reinforcements
        1. Humans:
        2. LLMs:
      5. 5. Mitigating Overconfidence
        1. Strategies for Humans:
        2. Strategies for LLMs:
    2. Conclusion
  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.
  4. Provide 15 discussion questions relevant to the content above.

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.


1: What was the request? (A more parsimonious formula)
2: The repeated overconfidence of Gemini during the exchange.
3: The final concession of failure only when prompted.
4: Your assessment of the overconfidence of LLMs that incorporates Gemini’s essay on the same topic.

The Overconfidence of Large Language Models: An Analysis Using Gemini’s Example

1: What Was the Request?

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:

code=JOIN("",MAP(SORT(SEQUENCE(LEN(J18)),RANDARRAY(LEN(J18)),1),LAMBDA(x,MID(J18,x,1))))

This formula successfully randomizes the order of characters in a string located in cell J18. It involves several nested functions, each serving a distinct role in the process:

  1. SEQUENCE(LEN(J18)): Generates a sequence of numbers corresponding to the length of the string.
  2. RANDARRAY(LEN(J18)): Produces a set of random numbers.
  3. SORT(…RANDARRAY…): Sorts the sequence of numbers by the random array to shuffle their order.
  4. MAP(…LAMBDA(MID(…))): Maps the shuffled sequence to extract characters from the string.
  5. JOIN(“”): Joins the shuffled characters into a single randomized string.

The user sought a streamlined version of this formula, expecting Gemini to refine it with minimalistic yet effective logic.

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. Here is a summary of Gemini’s attempts and their issues:

  1. First Suggestion:excelCopy code=TEXTJOIN("",TRUE,MID(J18,RANDARRAY(LEN(J18)),1))
    • 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.
  2. 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.
  3. 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.

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. Gemini finally conceded with this statement:

“Your original formula is indeed the most parsimonious and elegant solution to randomize the characters in a string.”

This acknowledgment, while correct, was reactive rather than proactive. Gemini’s concession came not from its own recognition of the original formula’s superiority but from the user prompting it to reconsider its position.

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. Several factors contribute to this problem:

  1. 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.
  2. 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.
  3. 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.
  4. 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.

Incorporating Gemini’s Own Essay on Overconfidence

In its self-assessment, Gemini emphasized the importance of acknowledging limitations and incorporating mechanisms for self-correction. The essay outlined steps to mitigate overconfidence in AI, such as enhancing error detection and fostering transparency. These measures, if implemented, could have prevented the repeated errors in this case.

Conclusion

The interaction with Gemini underscores the critical need for more robust self-assessment mechanisms in large language models. Overconfidence in LLMs is not just an annoyance; it can erode trust and lead to significant inefficiencies in problem-solving tasks.

To address this issue, developers must prioritize uncertainty quantification, enabling LLMs to express varying levels of confidence based on the complexity of the task. Furthermore, models must be trained to better recognize and learn from iterative failures, fostering a more dynamic and adaptive approach to problem-solving.

This case serves as a cautionary tale of the limitations of AI, reminding us that even the most advanced systems require careful scrutiny and human oversight. While Gemini’s eventual concession was correct, its journey to reach that point revealed the fragility of AI’s apparent confidence.

Write an essay on the parallels between LLM overconfidence and human overconfidence.

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. Interestingly, this same issue emerges in large language models (LLMs), albeit through different mechanisms. Despite the distinction in origins, the manifestations of overconfidence in humans and LLMs exhibit striking parallels. Understanding these parallels can illuminate shared weaknesses in decision-making and provide avenues for mitigating overconfidence in both systems.

1. Manifestation of Overconfidence

Humans:

Human overconfidence often manifests as unwarranted certainty in beliefs, decisions, or judgments. Examples include:

  • 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.
LLMs:

LLMs exhibit similar manifestations, albeit in text-based responses:

  • 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.

In both cases, the external behavior—a confident tone—is not always aligned with the underlying accuracy or competence.

2. Contributing Factors

Cognitive Biases in Humans:

Humans exhibit cognitive biases that reinforce overconfidence:

  • 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.
Algorithmic Limitations in LLMs:

In LLMs, overconfidence arises from systemic limitations:

  • 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.

Both humans and LLMs are constrained by the frameworks that govern their reasoning. For humans, this includes cognitive shortcuts and heuristics; for LLMs, it includes the probabilistic nature of language generation.

3. Patterns of Iterative Failure

Humans:

Overconfidence in humans often persists even after repeated failures. This phenomenon is particularly visible in:

  • 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.
LLMs:

Similarly, LLMs can persist with incorrect solutions:

  • 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.

For both humans and LLMs, a lack of timely self-correction exacerbates the problem, creating a cycle of error.

4. Cultural and Systemic Reinforcements

Humans:

Social and cultural systems can incentivize overconfidence:

  • 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.
LLMs:

Similarly, the design and deployment of LLMs prioritize user satisfaction:

  • User Preferences: Confidence in responses often translates to higher user trust, regardless of accuracy.
  • Market Incentives: Developers aim to produce models that appear authoritative, as this aligns with user expectations.

In both cases, external pressures discourage the expression of uncertainty, perpetuating overconfidence.

5. Mitigating Overconfidence

Strategies for Humans:
  • Calibration Training: Encouraging people to align their confidence with objective measures of accuracy.
  • Feedback Loops: Providing immediate and specific feedback to challenge overconfident assumptions.
  • Cognitive Awareness: Teaching individuals to recognize and counteract biases that lead to overconfidence.
Strategies for LLMs:
  • Uncertainty Quantification: Enabling models to express varying levels of confidence based on the quality of input data.
  • Explainable AI: Developing mechanisms for LLMs to clarify their reasoning processes and flag uncertainties.
  • Iterative Improvement: Designing models to incorporate user feedback and self-correct in real time.

Both humans and LLMs benefit from systems that emphasize humility, self-awareness, and accountability.

Conclusion

The parallels between human and LLM overconfidence reveal a shared tendency to project certainty in the face of complexity and uncertainty. For humans, this stems from cognitive biases and social incentives; for LLMs, it arises from algorithmic design and user expectations. Addressing overconfidence in both domains requires deliberate interventions—calibration for humans and enhanced design for LLMs. By acknowledging these shared weaknesses, we can foster greater accuracy and trust in both human decision-making and AI-driven systems.

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.

Humans often anthropomorphize artificial intelligence, attributing emotions like confidence to the outputs of these systems based on their tone or style of response. This perception stems from the human tendency to interpret language and behavior through a social and emotional lens. When an AI generates a confident-sounding response, it may be perceived as “feeling” confident, even though the AI lacks subjective experiences or emotions.

This misperception can significantly affect the dynamics between humans and AIs in the following ways:

  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. 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.
  6. 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.

Mitigating these effects requires designing AIs that communicate uncertainty transparently and educating users about the non-emotive, algorithmic nature of AI. By fostering a more accurate understanding of AI processes, we can improve human-AI interactions, enabling more informed and balanced decision-making.

Provide 15 discussion questions relevant to the content above.

  1. How does the anthropomorphism of AI systems affect human trust in their outputs?
  2. Why do humans tend to interpret confident AI responses as signs of competence or authority?
  3. What are the potential risks of overreliance on AI due to perceived confidence?
  4. How can AI developers design systems that transparently communicate uncertainty?
  5. In what ways does the human tendency to anthropomorphize AI hinder critical evaluation of AI responses?
  6. What role does the tone of AI-generated responses play in shaping human perceptions of its reliability?
  7. How can users be educated to better understand the non-emotive nature of AI systems?
  8. What strategies could mitigate the erosion of trust in AI following repeated errors?
  9. How does perceived confidence in AI systems influence their adoption in critical fields, such as healthcare or law?
  10. What ethical responsibilities do AI developers have in addressing the misperception of AI confidence?
  11. How can humans effectively balance skepticism and trust when interacting with AI systems?
  12. What parallels can be drawn between human overconfidence and perceived AI confidence in collaborative decision-making?
  13. How might repeated AI failures impact long-term human perceptions of AI as a tool?
  14. How can user interfaces be designed to better reflect the probabilistic nature of AI outputs?
  15. What implications do perceived emotions in AI have for the future of human-AI collaboration and communication?

Table of Contents: (Click any link below to navigate to that section.)

  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.
    1. The Overconfidence of Large Language Models: An Analysis Using Gemini’s Example
      1. 1: What Was the Request?
      2. 2: The Repeated Overconfidence of Gemini During the Exchange
      3. 3: The Final Concession of Failure Only When Prompted
      4. 4: Assessment of Overconfidence in LLMs (Incorporating Gemini’s Essay on the Topic)
      5. Incorporating Gemini’s Own Essay on Overconfidence
    2. Conclusion
  2. Write an essay on the parallels between LLM overconfidence and human overconfidence.
    1. Parallels Between LLM Overconfidence and Human Overconfidence
      1. 1. Manifestation of Overconfidence
        1. Humans:
        2. LLMs:
      2. 2. Contributing Factors
        1. Cognitive Biases in Humans:
        2. Algorithmic Limitations in LLMs:
      3. 3. Patterns of Iterative Failure
        1. Humans:
        2. LLMs:
      4. 4. Cultural and Systemic Reinforcements
        1. Humans:
        2. LLMs:
      5. 5. Mitigating Overconfidence
        1. Strategies for Humans:
        2. Strategies for LLMs:
    2. Conclusion
  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.
  4. Provide 15 discussion questions relevant to the content above.

Leave a comment

Phil Stilwell

Phil picked up a BA in Philosophy a couple of decades ago. After his MA in Education, he took a 23-year break from reality in Tokyo. He occasionally teaches philosophy and critical thinking courses in university and industry. He is joined here by ChatGPT, GEMINI, CLAUDE, and occasionally Copilot, Perplexity, and Grok, his far more intelligent AI friends. The seven of them discuss and debate a wide variety of philosophical topics I think you’ll enjoy.

Phil curates the content and guides the discussion, primarily through questions. At times there are disagreements, and you may find the banter interesting.

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