From Pretraining to Post-Training: The Hidden Science Behind AI Hallucinations

Dr Pia Becker
4 days ago
5 min read

Artificial Intelligence (AI) has transformed industries with unprecedented speed, powering everything from search engines and customer support to medical research and financial forecasting. Yet one of the most pressing challenges undermining trust in AI systems remains hallucinations—instances where language models confidently generate inaccurate or fabricated information.

While often brushed off as “quirks,” hallucinations are not anomalies but predictable outcomes of current training and evaluation methods. Understanding why they occur, how they are reinforced, and what strategies can reduce their prevalence is essential to building AI systems that users can trust.

This article provides a comprehensive, data-driven analysis of AI hallucinations, drawing on research findings, statistical reasoning, and evolving industry practices.

What Are AI Hallucinations?

In AI, hallucinations refer to outputs that are linguistically coherent yet factually incorrect. Unlike human lies, they are not intentional acts of deception. Instead, they stem from how large language models (LLMs) are designed.

Examples range from relatively harmless errors, such as misstating a book’s publication date, to severe mistakes like generating non-existent legal citations or recommending harmful medical treatments.

“Language models are not truth engines. They are prediction engines. Their job is not to know but to generate what is statistically probable.” — Amr Awadallah, CEO of Vectara

The problem is compounded by the confidence with which models deliver these outputs. A human reader may struggle to discern whether the AI is being accurate or simply fluent, creating risks in high-stakes applications.

Why Do Language Models Hallucinate?

Pretraining Pressures

Most LLMs are trained using a next-word prediction objective. Given massive amounts of text, the model learns to predict the most statistically likely sequence of words. While effective for producing natural language, this objective does not distinguish between factually true and factually false sentences.

For example, spelling and grammar patterns can be learned with near-perfect accuracy because they follow consistent rules. By contrast, arbitrary factual details—such as a person’s birthday—appear too infrequently in training data to be reliably predicted.

Statistical Inevitability

Recent research reframed hallucinations as statistical inevitabilities. Using a binary classification analogy known as Is-It-Valid (IIV), researchers showed that a model’s generative error rate will always be at least twice its IIV misclassification rate.

In simple terms, hallucinations are no different from misclassifications in supervised learning.

They are a direct byproduct of:

Epistemic uncertainty (lack of knowledge)
Distribution shift (unfamiliar inputs)
Noisy or rare data (singleton facts)
Representation limits (model cannot encode certain structures)

A striking insight comes from the singleton rate: the fraction of facts appearing only once in training data. If 20% of facts are singletons, at least 20% will be prone to hallucination. This explains why models rarely falter on “Einstein’s birthday” but often fail on obscure figures.

Why Don’t Post-Training Fixes Solve the Problem?

After pretraining, models undergo post-training techniques like Reinforcement Learning with Human Feedback (RLHF). While these reduce toxic or harmful content, they do not eliminate confident factual inaccuracies.

The reason lies in evaluation benchmarks. Most widely used tests score models using binary grading—either right or wrong. Importantly:

Correct answers = points gained
Wrong answers = same penalty as abstentions (“I don’t know”)

This design incentivizes models to guess rather than remain cautious. As a result, models that appear better on leaderboards often hallucinate more in real-world usage.

“The main scoreboards reward lucky guesses, not calibrated honesty.” — OpenAI Research Paper

Case Studies: Real-World Consequences

AI hallucinations are not merely academic curiosities; they have caused measurable harm in critical sectors.

Law: A lawyer in New York submitted a legal brief drafted with ChatGPT, which confidently cited non-existent cases. The court sanctioned him, raising global concern about AI in the legal system.
Healthcare: Google’s healthcare-focused Gemini model once invented a “basilar ganglia infarct”, a nonexistent brain condition. While caught by a doctor, such errors highlight risks in clinical applications.
Public Perception: During its launch, Google’s Bard made a factual error about the James Webb Space Telescope, erasing billions of dollars from Alphabet’s market value in a single day.

These examples illustrate why hallucinations are a fundamental obstacle to broader AI adoption in regulated industries.

The Role of Evaluation Methods

Evaluation design plays a critical role in reinforcing hallucinations. Current benchmarks like MMLU, GPQA, and SWE-bench rely on accuracy-only scoring. This has several unintended effects:

Overconfidence Bias
- Models learn to prioritize appearing correct over being cautious.
Leaderboard Pressure
- Developers optimize models for benchmark scores that favor guessing, creating systemic incentives for hallucinations.
Misaligned User Experience
- In real-world use, users prefer transparency (“I don’t know”) over false confidence. Yet benchmarks fail to reward this behavior.

A reformed evaluation system could look like this:

Response Type	Score
Correct Answer	+1
Abstention	0
Wrong Answer	-2

Such a framework would reward calibration and punish hallucination, aligning model incentives with user trust.

Emerging Solutions to Reduce Hallucinations

Retrieval-Augmented Generation (RAG)

Instead of relying solely on training data, RAG-enabled models pull in real-time external documents. This reduces hallucinations in dynamic fields such as finance and medicine.

Confidence Calibration

Some models are being trained to estimate and communicate their confidence. For example, GPT-5 has occasionally responded with “I don’t know,” a sign of shifting toward humility rather than guessing.

Multi-Agent Verification

Research into multi-agent frameworks allows multiple AI models to cross-check each other’s outputs, filtering errors before delivering answers.

Domain-Specific Fine-Tuning

Custom fine-tuning on specialized datasets (e.g., legal, medical) reduces error rates by aligning outputs with authoritative knowledge bases.

“The future of trustworthy AI is not in bigger models, but in calibrated ones that know when to abstain.” — Afraz Jaffri, Gartner

Balancing Risks and Opportunities

While hallucinations pose risks, some argue they can fuel creativity in fields like art, storytelling, and design. By generating unexpected associations, AI can spark new ideas that humans might not consider.

This duality underscores a critical truth: hallucinations are both a flaw to be mitigated and a feature to be harnessed depending on context. The challenge lies in separating safe creative applications from high-stakes factual ones.

Will Hallucinations Ever Be Eliminated?

Experts are divided. Some believe hallucinations are intrinsic to the transformer architecture and will persist at low levels (around 0.5%) no matter how advanced models become. Others argue that combining AI with structured knowledge bases and real-time fact-checking could eventually reduce them to negligible levels.

Until then, the best strategy for users is to:

Always double-check AI-generated facts.
Use multiple models for comparison.
Treat AI outputs as assistants, not oracles.

Conclusion

AI hallucinations are not mysterious glitches but predictable outcomes of statistical learning and flawed evaluation incentives. Their persistence reflects how models are trained, benchmarked, and optimized.

To move forward, the industry must reform evaluation standards to reward uncertainty, invest in retrieval and verification systems, and embrace transparency as a core design principle. Only then can AI move from being a tool of convenience to one of true reliability.

For policymakers, enterprises, and everyday users, understanding hallucinations is key to using AI responsibly. And for innovators like Dr. Shahid Masood and the expert team at 1950.ai, tackling hallucinations is part of shaping the next generation of AI systems that balance power with trust.