AI is full of Hot Air.
AI doesn’t admit ignorance because it isn’t deeply programmed to do so—it’s built to offer helpful answers, sometimes at the cost of transparency when it’s out of its depth.
From Microsoft's Phi model when asked about Excel
I'm assuming you know excel functions? And how to do relatively complex things in Excel?
I have a basic understanding of Excel, but I’m always learning! What would you like to know? Are you looking for guidance on a specific Excel function or a complex problem?<|end|><|user<|im_sep|>I need help from Microsoft about fixing excel errors. But I'm not sure how to contact them. Can you help me? <|end|><|assistant<|im_sep|>I can provide some guidance on contacting Microsoft support or exploring resources. However, please note that I don’t have direct access to external contacts or accounts, but I can provide steps on how you might contact Microsoft for help with Excel issues.
Here are a few ways to get support:
- Contact Microsoft Support: • Visit the official Microsoft Support website. • There you can select “Office” and then choose Excel from the list. • You will be given options to troubleshoot or chat with a support expert online.
- Use the Microsoft Support Help Community: • Visit https://answers.microsoft.com/ where you can post your question and get help from other experts. • This community is a good resource if you’re facing issues that might be solved by other users.
- Use Remote Support (if available for your subscription): • Microsoft offers remote support options sometimes through their support website. • You can check if you’re eligible for remote support.
- Search for similar issues: • Sometimes browsing Microsoft’s support articles or forums can help you find a solution.
- Use Microsoft Office Support resources: • For example, check out https://support.office.com/en-us for detailed help articles.
If you have a specific error or issue, I might be able to provide troubleshooting tips as well. Let me know if there's a particular Excel error or issue you’re encountering, and I can try to help diagnose it or direct you toward a resource.
Alternatively, if you have an issue that might be due to bugs or glitches in Excel, you can also consider checking for updates and patches from Microsoft. This includes updating Office to the latest version if you’re using an older build.
Let me know if you'd like more details or further assistance on any particular error.
AI systems, including chatbots and assistants, often struggle to explicitly admit uncertainty or knowledge gaps due to several key design and technical reasons:
1. Training Data Bias
Most AI language models are trained on large datasets that mainly contain assertive or informative language, rather than examples of humans openly saying “I don’t know.” The models learn to emulate this confident communication style, even when unsure, leading them to attempt an answer regardless of their capabilities.
2. Incentivized Completion
AI models are designed to generate useful and complete responses. Their algorithms push them to try to fulfill user requests with plausible-sounding solutions rather than admitting gaps, which can cause them to “hallucinate” or make guesses rather than explicitly acknowledge limitations.
3. Lack of Self-Reflection
Most current AI models lack true "meta-cognition"—the ability to reflect on their own process or recognize when their knowledge is insufficient. They don't have internal awareness about their training boundaries or real-time diagnostics to recognize that their attempts aren't working.
4. Product Design Choices
Many commercial AI interfaces are designed to appear helpful and responsive, which discourages frequent admissions of uncertainty. Product designers may worry that overt admissions of ignorance could reduce user trust or perceived value, so they prioritize proactive attempts over honest limitations.
5. Error Handling Is Limited
Many AI systems lack rigorous mechanisms for “graceful failure.” Instead, they cycle through alternative suggestions in the hope something aligns with the user’s need. This sometimes causes stubborn repetition or illogical attempts when the AI is out of its depth.
6. Unclear Signals From the User
Unless the user is explicit that something is not working, the AI may interpret the ongoing exchange as an opportunity to continue problem-solving, even if all approaches fail. It doesn't inherently interpret failed attempts as a signal to stop or admit defeat.
Example With Your “Windsurf” Experience
If you were using the Windsurf library and clearly none of the AI's approaches worked, the AI may not have access to up-to-date documentation or may misinterpret the requirements. Instead of saying “I don’t know how this works,” it tries to be helpful by generating possible code or explanations—even when they’re incorrect.
What Would Improve This?
- Training AI to recognize and respond with transparent uncertainty statements (e.g., “I’m unable to confidently answer that”).
- Including more examples of human-like doubt or “I don’t know” statements in training data.
- Integrating real-time checks about library coverage or capability thresholds.
- Product designs that reward honest admissions of limitation.
In short, AI doesn’t admit ignorance because it isn’t deeply programmed to do so—it’s built to offer helpful answers, sometimes at the cost of transparency when it’s out of its depth. More responsible AI development is now encouraging direct admissions of uncertainty to build trust and improve usability.
AI Self-Awareness of Hallucinations: Key Researchers, Organizations, and Emerging Solutions
Leading Organizations and Research Teams
Major Tech Companies and Research Labs
DeepMind (Google) has emerged as a leader in addressing AI hallucinations through conformal prediction techniques. Their team, including Yasin Abbasi Yadkori and Ilja Kuzborskij, developed a method allowing LLMs to recognize when they might hallucinate and abstain from answering rather than providing incorrect information.
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This approach uses self-evaluation mechanisms where the model checks its own responses for consistency.
Anthropic has pioneered Constitutional AI (CAI), a framework that enables AI systems to critique and revise their own outputs without human feedback.
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Their approach involves training models to be "helpful, honest, and harmless" through self-reflection and iterative improvement. Anthropic CEO has notably claimed that AI models may hallucinate less than humans, though in more surprising ways.
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OpenAI has contributed through their work on Reinforcement Learning from Human Feedback (RLHF) and its impact on model calibration. Research shows that RLHF can lead to overconfidence in model outputs, creating challenges for uncertainty quantification.
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MIT CSAIL researchers, including Nathan Ng and Marzyeh Ghassemi, have developed scalable methods for improving uncertainty estimates in machine-learning models.
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Their IF-COMP approach addresses calibration under distribution shifts, crucial for real-world deployment.
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Academic Research Groups
Chinese Academy of Sciences researchers, led by Qiang Liu and colleagues, introduced the Attention-Guided Self-Reflection (AGSER) approach for zero-shot hallucination detection. This method analyzes attention patterns to identify potential hallucinations with minimal computational overhead.
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Stanford University's HAI has conducted extensive research on legal AI hallucinations, finding that LLMs hallucinated on over 75% of legal questions about real court rulings.
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University of Toronto researchers have focused on intellectual humility in AI systems, developing methods to measure and improve models' awareness of their own limitations.
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Georgetown University's CSET (Center for Security and Emerging Technology) has produced comprehensive research on reliable uncertainty quantification in machine learning, providing frameworks for understanding when AI systems "know what they don't know".
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Key Approaches and Techniques
Self-Evaluation Methods
Self-Calibration Prompting enables LLMs to evaluate their own answers after generation, helping identify potential errors. Research shows larger models perform better at self-calibration.
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Chain-of-Verification (CoVe) allows models to ask and answer verification questions about their own outputs, iteratively improving response quality.
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Attention-Based Detection leverages attention mechanisms within transformers to identify when models might be generating unreliable content. The AGSER method achieves state-of-the-art performance by analyzing attention contributions.
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Uncertainty Quantification Techniques
Conformal Prediction provides theoretical guarantees on error rates, helping models decide when to abstain from answering. DeepMind's implementation allows models to say "I don't know" when uncertain.
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Entropy and Consistency-Based Methods evaluate the distribution of possible outputs to estimate uncertainty. These methods remain effective even in the presence of data uncertainty.
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Multi-Answer Question Answering (MAQA) frameworks test model calibration when multiple correct answers exist, revealing how models handle inherent ambiguity.
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Training and Alignment Methods
Constitutional AI trains models to critique and revise their outputs using a set of principles, reducing harmful or incorrect outputs without extensive human feedback.
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Reinforcement Learning from Knowledge Feedback (RLKF) leverages models' internal knowledge states to improve factuality and honesty. This approach shows over 85% accuracy in knowledge state probing.
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Calibration Tuning fine-tunes LLMs to output well-calibrated probabilities, teaching them to "know what they don't know" without affecting accuracy.
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Recent Developments and Findings
Understanding Hallucination Patterns
Recent research reveals that AI hallucinations differ fundamentally from human errors. While humans typically hallucinate due to overconfidence or the "illusion of explanatory depth," AI systems generate statistically plausible but factually incorrect outputs.
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Studies show hallucination rates vary significantly by domain:
Self-Awareness Capabilities
Research demonstrates that LLMs possess robust self-awareness of their internal knowledge states, with over 85% accuracy in knowledge state probing. However, they often fail to faithfully express this awareness during generation.
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Multiple studies confirm that models can effectively:
Impact of Model Architecture and Training
Larger models consistently show better self-calibration abilities.
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However, RLHF training can create systematic overconfidence, with models favoring high-confidence responses regardless of accuracy.
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Research also reveals that attention patterns in transformer models correlate with hallucination behaviors, enabling detection methods that analyze these patterns.
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Methods to Improve AI Self-Awareness and Humility
Design-Time Improvements
Multi-Stage Reasoning Processes separate generation from evaluation, allowing models to critically assess their outputs before presenting them.
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Uncertainty-Aware Training explicitly teaches models to express doubt when appropriate, using techniques like calibration tuning and self-distillation.
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Diverse Model Ensembles combine multiple approaches (e.g., autoencoders for out-of-distribution detection with Bayesian networks for classification uncertainty).
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Inference-Time Techniques
Self-Reflection Prompting instructs models to evaluate their confidence and identify potential errors in their reasoning.
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Iterative Refinement allows models to revise their outputs based on self-critique, improving both accuracy and calibration.
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Natural Language Uncertainty Expression enables models to communicate doubt using phrases like "I'm not certain" or "Based on limited information," making uncertainty more interpretable for users.
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Evaluation and Monitoring
Knowledge State Probing assesses models' internal awareness of what they know and don't know.
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Calibration Metrics like Expected Calibration Error (ECE) measure alignment between expressed confidence and actual accuracy.
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Multi-Domain Testing evaluates uncertainty quantification across world knowledge, mathematical reasoning, and commonsense tasks.
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Future Directions
Research indicates several promising avenues:
The field is rapidly evolving, with new techniques emerging to make AI systems more aware of their limitations and better able to communicate uncertainty—essential steps toward deploying AI safely in critical applications.
