How to reduce large language model hallucinations

LLM hallucination is the phenomenon where large language models (LLMs) generate meaningless or inaccurate output that does not conform to real patterns or objects. These erroneous AI outputs stem from a variety of factors, including:

1. Overfitting: LLM learns noise and bias in training data as patterns, causing the model to perform poorly on test data produces incorrect output.

2. High model complexity: LLMs have high model complexity, which allows them to perceive non-existent correlations, thereby creating illusions.

Major companies developing generative AI systems are taking steps to address the problem of AI hallucinations, although some experts believe it may be impossible to completely eliminate erroneous output.

Google connects its models to the internet to train on ground responses from data and network information, thereby reducing overfitting.

OpenAI uses human feedback and reinforcement learning to refine ChatGPT’s output. They propose "process supervision" that rewards models for correct reasoning steps, not just the final answer. This can improve explainability, but some question its efficacy against fabrication.

Despite the risks of AI hallucinations, companies and users can still take steps to offset and limit their potential harm. Here are some ways to solve it:

Use high-quality training data

Using high-quality training data is the key to reducing artificial intelligence hallucinations. High-quality training data should be diverse, balanced, well-structured, and reflect real-world situations.

Clear Intended Use

Clearly defining the specific purpose and permitted uses of an AI system can help guide it away from hallucinatory content. Developers and users should clearly understand the functions and uses of artificial intelligence models and strictly adhere to them when using them.

Use data templates to guide artificial intelligence output

Using structured data templates can help artificial intelligence models generate output that conforms to expected patterns. These templates provide a consistent format for data input into the model and limit the scope of the model's inferences.

Limit Reaction

Setting constraints and limits on potential model outputs can reduce uncontrolled speculation. For example, you can define clear probability thresholds and use filtering tools to filter out responses that do not meet expectations.

Continuously test and improve the system

Through comprehensive testing and continuous monitoring, the performance of the artificial intelligence system can be continuously improved. Evaluating the output can identify areas that need tweaking, while new data can be used to retrain the model and update its knowledge.

Rely on human supervision

Including human supervision can provide critical protection. When human experts review the output, they can capture and correct any illusory content through contextual judgment.

Idea Prompt Chain

Idea Prompt Chain is a technology that helps artificial intelligence models perform multi-step reasoning by providing a logical thinking chain. This approach can improve the performance of artificial intelligence models in tasks such as mathematics.

Task decomposition and agency

Task decomposition and agency is a method to improve the performance of artificial intelligence models by decomposing complex tasks into multiple subtasks. This method can take advantage of the advantages of different artificial intelligence models and improve the reasoning capabilities of the artificial intelligence models.

Artificial Intelligence Illusion is a challenge for the development of artificial intelligence, but by taking effective measures, its risks can be effectively reduced.

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