The Science of AI Hallucinations—and How Engineers Are Learning to Curb Them

When ChatGPT insists that the Eiffel Tower was “moved to Marseille in 2024,” that’s not AI creativity—it’s a hallucination.

Hallucinations are false statements generated confidently by an AI. In academic terms, they’re a side effect of the probabilistic nature of language models. Unlike humans who pause when unsure, models are designed to always answer—even when guessing.

There are two major types:

• Factual hallucination: Outputs contradict reality (e.g., “honey helps diabetics lower blood sugar”).
• Faithfulness hallucination: Outputs drift from the user’s intent (e.g., answering about nutrition when asked about diabetic sugar substitutes).

Why Hallucinations Happen: Four Core Mechanisms

1. Bad Data, Bad Knowledge

Data is the foundation of every model’s “understanding.”When training data is incomplete, outdated, or biased, the model simply guesses.

In long-tail or low-resource topics—say, a rare medical syndrome—the model might never have seen relevant data, so it fills the gap using probability patterns from unrelated text. That’s where “zero-resource hallucinations” emerge.

Duplicate data also reinforces noise. If a flawed statement (“Vitamin C cures COVID”) appears often enough, the model begins to treat it as statistically true.

2. Training Objectives That Reward Guessing

Early LLM training rewarded “getting something right” but didn’t penalize “being confidently wrong.”That’s like an exam where students earn points for right answers but lose nothing for wild guesses.

This binary scoring makes models reckless. They’d rather fabricate an answer than stay silent.

OpenAI discovered this during early fine-tuning: models often “invented” nonexistent functions or events simply because their training never encouraged saying “I don’t know.”

Modern approaches—like Anthropic’s constitutional AI and DeepMind’s triple reward systems—now introduce “I don’t know” as a valid, even rewarded, behavior.

3. Architectural Limits: The Attention Dilution Problem

Transformer-based models still struggle with long-term reasoning.When processing huge contexts, their attention mechanism dilutes key information. The result: logical drift.

Ask an LLM how Antarctic ice melt affects African agriculture—it might generate poetic nonsense because it fails to hold distant causal links in memory.

New architectures like Gemini 2.5 Pro’s hybrid attention (Transformer + PathFormer) aim to counteract this, dynamically balancing global and local focus. But architectural hallucination is far from solved.

4. Inference Chaos: Small Errors, Big Consequences

During generation, each token depends on the one before it.If the model mispredicts even once (“Paris → Berlin”), that error cascades—creating what researchers call a “logic avalanche.”

Since models lack built-in verification, they can’t double-check their own claims.As one NUS paper put it: “A language model cannot know when it doesn’t know.”

Fixing the Glitch: How We’re Learning to Control Hallucinations

1. Clean, Smart Data

Better data beats bigger data.

Projects like Concept7 evaluate concept familiarity during training—helping models know when they’re venturing into the unknown.Meanwhile, DeepSeek V3 reduces hallucination rates by 17% through real-time web grounding—keeping data fresh via live search integration.

Google’s Gemini filters redundant and contradictory content, while OpenAI experiments with generating synthetic counterexamples—feeding the model “tricky” fake data to improve fact-check resilience.

2. Smarter Architecture: Teaching Models to Think, Not Guess

Gemini 2.5’s multi-stage reasoning pipeline includes:

• Hypothesis generation
• Dynamic thinking depth (more steps for harder questions)
• Hybrid attention
• Closed-loop verification

It’s like giving the model a built-in Socratic method—ask, reason, verify, repeat.

This “thinking in public” design also enables observable reasoning chains through APIs, letting developers pinpoint where logic went off-track.

3. Reward Redesign: Make “I Don’t Know” a Win

The biggest behavioral shift comes from how we score models.

A new three-tier system changes the game:

This “anti-hallucination” scoring led to hallucination drops of 30–70% in medical and legal tasks.Even Anthropic’s Claude 3.5 now includes a “decline to answer” function—turning AI modesty into accuracy.

4. Uncertainty Detection: The SELF-FAMILIARITY Approach

Before generating, a model can now check its own familiarity with the topic.

Here’s a mini-implementation using Python + fuzzy matching to detect unknown concepts:

from fuzzywuzzy import fuzz

def detect_unknown_concepts(text, known_concepts, threshold=0.6):
    unknowns = []
    for word in text.split():
        if len(word) < 3: continue
        if max(fuzz.ratio(word.lower(), k.lower()) for k in known_concepts) < threshold * 100:
            unknowns.append(word)
    return set(unknowns)

medical_terms = ["glucose", "insulin", "carbohydrate", "diabetes"]
output = "Honey contains glycetin, which helps regulate diabetes."
print(detect_unknown_concepts(output, medical_terms))

Result:

{'glycetin'}

In real systems, such detection triggers a refusal response or re-verification step.

5. Retrieval-Augmented Generation (RAG): Marrying Memory and Search

When models don’t know something—let them look it up.

RAG combines retrieval with generation. Below is a simplified LangChain + Chroma example that turns PDFs into searchable AI knowledge bases:

from langchain.document_loaders import PyPDFLoader
from langchain.vectorstores import Chroma
from langchain.chains import RetrievalQA
from langchain.embeddings import OpenAIEmbeddings
from langchain.llms import OpenAI

loader = PyPDFLoader("medical_paper.pdf")
docs = loader.load()

vectorstore = Chroma.from_documents(docs, OpenAIEmbeddings(), persist_directory="./db")
qa = RetrievalQA.from_chain_type(
    llm=OpenAI(temperature=0),
    chain_type="stuff",
    retriever=vectorstore.as_retriever(search_kwargs={"k": 3})
)

query = "Can honey replace sugar for diabetics?"
answer = qa({"query": query})
print(answer["result"])

By grounding output in retrieved documents, Gemini’s “search anchoring” feature has cut factual errors down to 0.7%.

Real-World Hallucination Fixes: How Tech Giants Are Fighting Back

OpenAI: Rewarding Honesty

OpenAI’s new model behavior framework shifts scoring from “accuracy only” to “accuracy + honesty.”Models get positive reinforcement for acknowledging knowledge gaps, monitored by an internal Hallucination Leaderboard.

Google Gemini: Search Grounding

Gemini’s API uses google_search_retrieval() to pull fresh data, attaching metadata such as source, confidence score, and timestamp—keeping outputs current and verifiable.

Anthropic Claude: Legal Citations That Hold Up in Court

Claude’s “Quote & Verify” pipeline first extracts source quotes, then verifies them before responding.If the citation can’t be validated, it simply says: *“Unable to confirm this information.”*This system cut legal hallucinations to 0%, earning federal court endorsements in the U.S.

Evaluating Hallucinations: Benchmarks and Metrics

Tools like MiniCheck (400× cheaper than GPT-4 for fact verification) and FENCE (declaration-level fact scoring) are now standard in research pipelines.The new 2025 international evaluation framework even includes “wrong answers get penalties, refusals earn points.”

The Hard Truth: You Can’t Fully Eliminate Hallucinations

Even OpenAI admits that hallucinations are mathematically inevitable.But that doesn’t mean they’re unmanageable.

Future LLMs will balance accuracy and creativity via probabilistic governance—“hallucination masks” that dynamically control uncertainty.Meanwhile, lightweight local evaluators like MiniCheck-FT5 make hallucination detection practical even for small orgs.

As one Google researcher put it:

“The goal isn’t zero hallucination. It’s knowing when to doubt the machine.”

Final Thoughts

We’re entering a new era where AI systems must justify what they say, not just say it confidently.The next breakthroughs won’t come from bigger models—but from smarter governance: data hygiene, architecture reform, reward redesign, and self-awareness.

If hallucinations are AI’s dreams, the job of engineers is not to wake it up—but to teach it to know when it’s dreaming.