Understanding and Reducing Hallucinations in AI Language Models

AI language models have made remarkable progress, but they still sometimes produce answers that sound plausible yet are factually incorrect. These so-called hallucinations remain a significant challenge, even for the most advanced systems. Understanding why these errors occur is key to building more reliable AI tools. OpenAi recently gave us insight into this process and how to mitigate hallucinations.

What Triggers Hallucinations?

Hallucinations arise when a model confidently offers information that isn't true. Surprisingly, this can happen with both complex topics and basic questions, like an author’s birthday or dissertation title. Because the response sounds fluent and authoritative, it can easily mislead users.

How Evaluation Methods Encourage Guessing

One major factor behind persistent hallucinations is how we evaluate AI models. Most benchmarks prioritize accuracy, rewarding only correct answers and ignoring uncertainty. As a result, models are incentivized to guess rather than admit they don't know.

• Scoring systems often ignore or penalize uncertainty, making guessing the safer option for higher scores.
  
  
• Models are rarely penalized for wrong answers but may be "punished" for abstaining or expressing doubt.
  
  
• A system can appear highly accurate while still producing many confident mistakes.

For example, OpenAI found that a more cautious model with slightly lower accuracy actually made fewer hallucination errors than a riskier, higher-scoring model.

The Deep Roots: Next-Word Prediction and Data Limitations

AI models primarily learn to predict the next word in massive text datasets without always knowing what's factually correct. This focus on language patterns means models excel at sounding right, but not necessarily at being right.

• Models are trained to produce fluent, plausible text, not to verify facts.
  
  
• Frequent patterns, like grammar, are learned well; rare facts are often guessed.
  
  
• Training data usually lacks clear examples of wrong answers, making it hard for models to distinguish truth from fiction.

Later training steps try to address these issues, but if the original data doesn't clarify what's correct or incorrect, hallucinations can persist.

Rethinking Evaluation: Incentivizing Honesty Over Guessing

Researchers suggest a straightforward improvement: revise evaluation standards to reward honesty. If models get partial credit for uncertainty and face stricter penalties for confident errors, they're more likely to admit when they're unsure.

• Update benchmarks to value expressions of uncertainty, not just correct answers.
  
  
• Move beyond leaderboards focused solely on accuracy to include calibration and humility.
  
  
• Encourage widespread adoption of these improved metrics to align AI incentives with honesty.

This mirrors some standardized testing practices, where guessing is discouraged through penalties or partial credit for abstaining.

Dispelling Myths: What You Should Know

• Perfect accuracy isn't possible: Some questions simply don't have answers.
  
  
• Hallucinations are preventable: Models can be trained to abstain or express uncertainty.
  
  
• Bigger isn't always better: Smaller models may sometimes be more calibrated and cautious.
  
  
• Hallucinations are not mysterious bugs: They stem from how models are trained and evaluated.
  
  
• Evaluation needs to evolve: Real progress requires metrics that reward humility, not just correctness.

In My Experience

With over 5 years of working on AI models and now about of a year of Agentic AI, I have found that hallucinations are becoming rarer and likely meeting human performance at this point. Over the last year specifically I have noticed a rapid decrease in fictitious fact (FakeNews) generation in my blog AI agents for news and repository content creation. This is a combination of dedicated optimization in prompts and process as well as major steps forward of leading SOTA models.

I have found hallucinations are greatly reduced in leading SOTA models specifically (Gemini 2.5, GPT5, Grok Heavy) but are still a persistent problem in many open source models including Qwen and GPT-OSS. I attribute this to a combination of factors including system prompting and multi agent handling that the company chat apps use. While I have attributed reductions in hallucinations in the API requests due to a combination of increased model performance and heavy prompt and agent structure updates.

What has your experience been? I'd love hear your experience on Linkedin!

Conclusion

Reducing hallucinations in language models isn't just about building better AI—it's about changing how we evaluate and incentivize their performance. By rewarding models for honesty and careful uncertainty, we can make them more trustworthy and useful. OpenAI's research is a promising step forward, but ongoing effort and better evaluation standards will be essential for continued improvement.

Source: https://openai.com/index/why-language-models-hallucinate/