Executives ask us all the time: Why do LLMs hallucinate? Why do they sometimes give confident, wrong answers?

The short answer: because we’ve been rewarding them for answering anyway. Most industry benchmarks and leaderboards give points for correct answers and give zero credit when a model says “I don’t know.” Over thousands of test items, a model that “guesses” will usually outscore a cautious model that abstains. That incentive quietly pushes systems to answer even when they’re unsure. Great for leaderboards, bad for reliability.
OpenAI: Why language models hallucinate (blog) · OpenAI: Why Language Models Hallucinate (paper) · Computerworld coverage

Takeaway: Hallucination isn’t just a bug; it’s an incentive problem. If scoreboards favor guessing, models learn to guess.

What Is AI Hallucination?

AI hallucination (sometimes called confabulation) is when a model produces a fluent but false statement like inventing a source, a date of birth, or a regulation that doesn’t exist.
Everyday examples:

• Citing a journal article that was never published.

• Making up an API parameter.

• Confidently giving the wrong birthday for a public figure.

Why it happens in plain English: models are trained to predict likely text. When facts are rare, ambiguous, or not in training data, they fill in the blanks with something that looks right.
TruthfulQA benchmark

The Incentive to Guess: How Today’s Evals Reward “Answering Anyway”

Most widely used evaluations grade like a school test: right = 1, wrong or abstain = 0. That means a model that never admits uncertainty can appear better than a model that refuses low-confidence questions.

A simple illustration: If you ask a model for someone’s birthday and it guesses, it has a 1/365 chance of being right. Saying “I don’t know” gets 0 points. Over thousands of questions, the guesser wins the scoreboard even if it’s wrong far more often in real use.
OpenAI blog summary · OpenAI paper (evaluation incentives) · Computerworld: binary grading encourages guessing

This is what researchers call “teaching to the test.” If accuracy is the only headline metric, teams will optimize for it even when calibrated uncertainty would be safer.

Why Do AI Models Hallucinate?

• Sparse or one-off facts. Some facts occur rarely in training data. Without repeated signal, the model can’t separate truth from plausible noise.
  TruthfulQA

• Evaluation pressure. If abstaining gets no credit, systems that answer more look better on paper.
  OpenAI (evaluation incentives)

• Human-feedback bias. Preference learning can unintentionally favor confident, agreeable responses over cautious truthfulness (often called sycophancy).
  Anthropic explainer · Anthropic paper

• Over-refusal whiplash. When models refuse too often, users complain, so providers tune back toward answering; the pendulum can swing past “honest uncertainty.”
  OR-Bench: Over-Refusal Benchmark

The Business Risk: “Looks Great in a Benchmark, Fails in My Workflow”

Leaders tell us their pilots “ace the demo” but struggle in production. Three common failure modes:

• Confident wrongs in low-data corners. Internal acronyms, new policy changes, or edge cases trigger fluent but false outputs.
  OpenAI blog

• Tool misuse. An agent “hallucinates” the existence of a database field or API method and chains that error forward.
  AbstentionBench: knowing when not to answer

• Trust erosion. One polished yet wrong answer can tank adoption among front-line staff and auditors.
  TruthfulQA: deceptive fluency risk

Better Benchmarks (What Good Looks Like)

If we change how we score models, we change how they behave.

Reward calibrated uncertainty

Add credit for appropriate “I don’t know” and penalize confident errors more than abstentions. (Think: replacing pure accuracy with accuracy plus calibration/abstention scoring.)
OpenAI: proposed fixes · AbstentionBench

Test the ability to abstain

Adopt benchmarks that explicitly include unanswerable, underspecified, or outdated questions, and measure whether models withhold answers appropriately.
AbstentionBench

Don’t chase only “vibes” leaderboards

Arena-style leaderboards are useful signals, but they shouldn’t be the only KPI. Favor transparent, reproducible evaluations and include business-relevant tasks with auditable scoring.
TruthfulQA · OR-Bench

How to Reduce LLM Hallucinations (Practical Playbook)

Here’s what we implement with clients to reduce LLM hallucinations without killing usefulness:

Tune behavior, not just answers

• Abstention prompts & policies. Teach the model when to ask for clarification or decline.

• Confidence gating. Use a calibrated confidence or verifier to route: answer / cite / ask / escalate.

• Negative marking in evals. During offline evaluation, punish confident errors to reshape the objective.
  OpenAI blog: reward design

Ground the model

• Retrieval-augmented generation (RAG) from a curated, versioned knowledge base (not a raw file dump).

• Tooling with typed contracts. Tools (DB/API) with schemas and guardrails reduce free-text fabrication.
  TruthfulQA: grounding reduces misconception mimicry

Observe and improve

• Trace everything. Log prompts, retrieval, tools, and final answers so you can audit decisions.

• Drift & freshness checks. Re-index policies and SOPs; flag stale sources.

• Targeted red-teaming. Attack the model where abstention is expected (edge cases, unknowns).
  AbstentionBench

Where the Field Is Heading (and What to Ask Your Vendors)

What to look for in 2025–2026:

• Abstention-aware benchmarks becoming part of standard model cards.

• Calibration metrics (e.g., proper scoring rules) alongside accuracy.

• Fewer “one number” scoreboards; more multi-metric dashboards (accuracy, abstention precision/recall, citation validity).
  OpenAI paper · OR-Bench · TruthfulQA

Questions to ask vendors:

• How does your model score when abstentions are rewarded and confident errors are penalized?

• Do you evaluate on unanswerable or underspecified questions?

• Can your system route low-confidence cases to humans or ask for clarification?

How Genta Helps (and why this matters to you)

At Genta, we design, build, and operate AI that knows its limits:

• Reliability by design. Confidence gating, abstention prompts, and escalation paths—wired into the runtime.

• Abstention-aware evals. We re-score your models with negative marking for confident errors and partial credit for honest uncertainty.

• Production observability. End-to-end traces for prompts, retrieval, and tools so risk and audit can see what happened.

• Faster, safer rollout. Start with one critical workflow, publish KPIs (error rate, time saved, cost/tx), then scale.

Talk to an Expert | See How We Build Reliable AI

References & Further Reading

• Why language models hallucinate — OpenAI’s plain-English explanation of the guessing incentive and how to fix scoring.
  OpenAI blog · Research paper (arXiv) · Computerworld

• Sycophancy in RLHF models — Why human-preference training can favor confident agreement over truth.
  Anthropic research overview · Paper (arXiv)

• TruthfulQA — A benchmark that measures truthfulness vs. human misconceptions, highlighting that more capable models can still be less truthful.
  ACL Anthology · Paper (arXiv)

• AbstentionBench — Tests whether models know when not to answer across diverse scenarios.
  Paper (arXiv)

• OR-Bench (Over-Refusal) — Because refusing too much also harms usefulness; balances the other side of the trade-off.
  Paper (arXiv)