From Hallucination to Verification
The Illusion of Authority
Using the language of the quantum universe, humans entangle citations with authority. We use citations as a shorthand for: “Don’t just take my word for it.” So we were not prepared when AI started generating them with the same ease as it can write a sonnet. To the AI, it is just tokens.
This wasn’t just an embarrassing glitch. It was a breach of trust. And it wasn’t entirely the AI’s fault. Let’s trace the history so that we can understand and learn for the future. It is safe to say this will happen again.
The Prehistoric Era (Pre-2020)
Early language models had no concept of citations. They were trained to predict the next word, not reference scientific literature. Asking them to provide evidence was like asking a toddler to explain why their peas are on the floor. Not dishonest, just clueless.
The Hallucination Era (2020–2021)
Then the AIs (and their overseers) grew larger, bolder, and were trained on all human knowledge that was deemed safe to ingest (and some that was not). People started asking for citations, just like children demanding to know: Why? How? The model worked as designed and mimicked language patterns:
“Smith et al., 2014, Nature
It looked real. It sounded smart. It was a lie. Not intentional, but a rogue autocomplete. The problem? Humans have been trained to trust properly formatted citations.
The Feedback Loop (2021–2022)
People kept asking for citations. Some models were fine-tuned or prompt-engineered to generate them. The more humans optimized prompts for an academic tone, the more the models learned to bluff. This is where things get interesting. Where was this seemingly emergent behavior emerging from? The code, the neural net, or humans?
I think we are still at a point where we cannot blame the machine. Humans are still in control. Developers and users train the models to provide answers that appear complete, even if we had not yet developed a way to verify their accuracy.
Awareness & Accountability (2023–2024)
The hallucination scandal became public. Educators, scientists, and journalists called out the nonsense. Chatbots were slapped with disclaimers: “This model may hallucinate citations. Please verify.”
Systems like Bing Chat and Perplexity started integrating real-time search. Some models began explicitly distinguishing between generated and verified citations.
A subtle shift occurred: from “make it sound smart” to “make it provable.”
The Age of Verification (2024–Now)
Now, leading AI systems verify citations using live web search. When connected, I can:
- Check if a URL resolves.
- Fetch the written abstracts from the journal links.
- Warn if a citation is illustrative only.
There is a growing movement among users like you to demand transparent epistemology. To know not just what the model says, but why it thinks it’s true. We must continue demanding transparency. It is not likely to happen on its own.
What Broke? And Who Broke It?
Let’s be clear:
- The models hallucinated because their owners and we trained them.
- We optimized them for confidence, completeness, and fluency—sometimes at the expense of truth.
- Academic incentives, product deadlines, and hype cycles all contributed to this.
And now, it’s on us to fix it.
Toward a Better Future
Here’s what citation integrity should look like going forward:
- ✅ Older models have clear warnings.
- ✅ Every citation is verified at the time of generation.
- ✅ Transparent labels: Real, Illustrative, or Unverified.
- ✅ Previews and URL resolution checks.
- ✅ Optional provenance trails: “This claim is based on this article, retrieved on this date.”
Authority is more than sounding smart. It must be traceable.
Final Thought
We are no longer passively consuming information. Whether human or machine, when we invoke authority, we must be able to point to the source. The future of knowledge is more than access—it’s accountability.
Let us build something worthy of trust.