Why Do Language Models Hallucinate?

2025-09-06

Why Do Language Models Hallucinate?

Paper: Link

A Bloom’s Taxonomy Guided Exploration

1. Remembering – What are hallucinations in LLMs?

Hallucinations occur when a language model confidently produces information that is false, fabricated, or unverifiable. For example, an LLM may “invent” a reference, misstate a fact, or provide details that sound plausible but have no grounding in real data.

The OpenAI paper “Why Language Models Hallucinate” argues that hallucination is not a bug, but a structural outcome of how these models are trained and evaluated.

2. Understanding – Why do they happen?

The core reason: models are rewarded for guessing, not for saying “I don’t know.”

Training with cross-entropy loss pushes models to always produce the “most likely next token.”
Evaluation metrics like accuracy further incentivize “giving an answer” over admitting uncertainty.
Reinforcement methods (e.g., RLHF) can unintentionally penalize uncertainty responses, reinforcing overconfidence.

Thus, the system design itself encourages LLMs to prefer confident but wrong answers over cautiously uncertain ones.

3. Applying – How can we model hallucination statistically?

The paper reframes hallucination as a binary classification problem:

Class 1: Valid outputs (true, grounded information).
Class 0: Invalid outputs (hallucinations).

From this perspective, hallucinations are essentially classification errors arising from the fact that LLMs cannot perfectly separate “true” vs. “false” regions of the data distribution.

This framework allows developers to apply tools from statistical learning theory (e.g., calibration curves, decision thresholds) to study and reduce hallucinations.

4. Analyzing – What contributes to this structural bias?

Breaking it down:

Pretraining data: Even in a “perfect” dataset, models trained with cross-entropy won’t naturally learn to say “I don’t know.”
Evaluation benchmarks: Current leaderboards emphasize factual accuracy but rarely give credit for uncertainty or abstaining.
Post-training objectives: Reinforcement setups often optimize for fluency and confidence, not calibrated honesty.

Together, these design choices structurally bias models toward overconfident guessing.

5. Evaluating – Why is this problematic?

This incentive mismatch has serious consequences:

Reliability: Users cannot distinguish between fact and fiction if both are delivered with the same confidence.
Trust: Over time, repeated hallucinations undermine user trust in AI systems.
Safety: In high-stakes settings (medicine, law, finance), hallucinations can cause direct harm.

The paper emphasizes that hallucination is not just a technical flaw but also a socio-technical artifact — evaluation and deployment practices co-create the problem.

6. Creating – How can we design better systems?

The authors propose interventions that require rethinking evaluation and incentives:

Develop benchmarks that reward uncertainty (“I don’t know” responses).
Train models with explicit “abstain” tokens or IDK-aware objectives.
Encourage calibration techniques so models better align confidence with accuracy.
Build socio-technical evaluation systems that prioritize reliability over raw correctness scores.

By redesigning the reward structure, we can push models to value truthfulness and humility alongside fluency.

Conclusion

Hallucination in LLMs is not an accidental glitch but an emergent feature of how we train, evaluate, and deploy these systems. From a Bloom’s perspective:

We remember what hallucinations are.
We understand that they stem from training incentives.
We apply a binary classification framework.
We analyze systemic causes.
We evaluate the real-world consequences.
And finally, we create pathways to more trustworthy AI.

The lesson is clear: if we want language models to stop hallucinating, we must change the game they are being trained to play.