How to reduce hallucinations in large AI models

Hallucinations – that is, false or fabricated information – often occur in large language models like ChatGPT. However, there is hope, as targeted prompt engineering can significantly reduce this weakness.

In this article, you will learn how current research and proven strategies improve the performance of language models. We also show the limitations in the practical application of these methods and what to watch out for.

What are hallucinations in language models?

Before we explain the techniques of prompt engineering, we first need to understand what hallucinations mean in this context. Language models like ChatGPT are based on probability calculations and therefore sometimes provide false or fabricated information. This problem is particularly critical in fields such as medicine, law, and software development, where precise information is crucial.

How prompt engineering helps reduce hallucinations

The good news: prompt engineering is very effective and helps make hallucinations occur much less frequently. Numerous studies and experiments show that precise and context-rich inputs can significantly improve accuracy. A good example is the chain-of-thought technique, where the model must respond logically in multiple steps. This not only reduces errors but also results in much better coherence in the generated content. A recent study showed that this technique increased answer accuracy by 30 percent in math problems.

Best practices for optimizing prompts

A well-thought-out prompt strategy is essential to effectively reduce hallucinations. Here are some practical, proven approaches:

1. Precision in the prompt

The more precise and specific the input, the higher the chance of receiving an accurate answer. Instead of simply asking, “Tell me something about history,” it is better to formulate the question more specifically. A better question would be: “Describe the most important events of the French Revolution in three sentences and name a source.” This way, the model receives clear instructions, which significantly reduces the likelihood of speculation.

2. Assign a specific role to the model

By placing the model in a specific expert role, both accuracy and style improve. An instruction like “You are a historian” causes the model to respond more scientifically and precisely. This technique is especially effective when combined with other methods, such as providing sources.

3. Step-by-step thinking (chain of thought)

Requiring step-by-step thinking forces the model to construct its arguments in a logical and structured manner. This prevents hasty conclusions and keeps it closer to verifiable information. This method helps to reduce errors and achieve more accurate results, especially when dealing with complex tasks.

4. Retrieval of external data

The RAG method is particularly well suited when the model needs to use external sources to support its response. This prevents the model from making unreliable assumptions and relying on outdated information. Instead, it integrates current and verified facts directly into the response.

5. Self-assessment and verification

Incorporating self-criticism into the response process is another helpful method for improving quality. When the model questions and reviews its response, it can identify potential errors at an early stage. This technique significantly increases the accuracy of results, especially in complex situations.

Examples of use: Where hallucinations are particularly problematic

In many industries where precision is crucial, hallucinations can have serious consequences. This is especially evident in medicine, where false information can be dangerous. A study found that ChatGPT cited up to 47 percent fabricated sources in scientific texts. Such errors can, in the worst case, endanger patient safety. Therefore, it is important to connect the model with reliable sources and involve human experts.

In the legal field, hallucinations also pose a serious risk. In a well-known case, ChatGPT generated incorrect case citations for a lawsuit. Targeted prompt engineering using legal sources can significantly improve the accuracy of the responses.

Conclusion

In conclusion, prompt engineering is an effective tool against hallucinations in language models. Targeted inputs encourage the model to work precisely and use reliable sources. Self-review also helps noticeably reduce the error rate. Nevertheless, human oversight remains indispensable in safety-critical areas such as medicine or law.

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