What is hallucination?
AsscherAi reduces incorrect decisions by identifying hallucination in query results, such as when a purchasing manager asks about supplier lead times and the system generates a response not supported by the purchase order table, then flags it as potentially incorrect, allowing the manager to verify the information.
In practice, what is hallucination in the context of AsscherAi is when the system generates fluent output that is not supported by the data, a failure that can lead to incorrect decisions being made by users such as a category manager comparing two suppliers. Hallucination occurs when the system fills in gaps in the data with information that is not actually present, resulting in answers that are not grounded in reality. This can happen when the system is trying to generate a response to a question that is not fully answerable by the data, and instead of saying that it does not know, it makes an educated guess that turns out to be incorrect.
Definition and distinction
Hallucination is often confused with the related concept of inference, where the system uses the data to make a logical conclusion that is not explicitly stated. However, the key difference is that inference is based on the actual data, whereas hallucination is not. Inference is a useful feature of AsscherAi, as it allows users such as a shift supervisor to get answers to questions that are not directly answerable by the data. On the other hand, hallucination is a failure that can lead to incorrect decisions being made.
A simple example can illustrate the difference: if a user asks what the total sales for a particular region are, and the system responds with a number that is not actually present in the data, that is hallucination. But if the system uses the data on sales for individual stores in that region to calculate the total, that is inference.
Where hallucination applies
In a large organisation, hallucination can occur in a variety of contexts, such as when a user is trying to get an answer to a question that is not fully answerable by the data. For instance, if a user asks what the impact of a particular marketing campaign was on sales, and the system does not have access to all the relevant data, it may fill in the gaps with incorrect information. This can lead to incorrect decisions being made, such as allocating more budget to a campaign that is not actually effective.
Most people get wrong the idea that hallucination is a rare occurrence, and that it only happens in extreme cases. However, the reality is that hallucination can occur in many different contexts, and it is a problem that needs to be addressed in order to ensure that the system is providing accurate and reliable answers.
What hallucination does not do
One common misconception about hallucination is that it is a problem that is unique to AsscherAi, or that it is a result of the system being too advanced or too powerful. However, the reality is that hallucination is a problem that can occur in any system that is generating answers based on data, and it is not a result of the system being too powerful, but rather a result of the system not being able to accurately distinguish between what it knows and what it does not know.
Another misconception is that hallucination is a problem that can be solved simply by adding more data to the system. While having more data can certainly help to reduce hallucination, it is not a guarantee that the problem will be completely eliminated. The system still needs to be able to accurately distinguish between what it knows and what it does not know, and this requires careful tuning and testing.
Reducing hallucination with retrieval
One way to reduce hallucination is to use retrieval-based approaches, where the system is designed to retrieve answers from a large database of pre-existing information, rather than generating them from scratch. This can help to ensure that the answers provided by the system are accurate and reliable, and that they are based on actual data rather than guesses or inferences. For more information on how AsscherAi uses retrieval to reduce hallucination, users can visit the documentation page.
By using retrieval-based approaches, organisations can help to reduce the risk of hallucination and ensure that their users are getting accurate and reliable answers to their questions. This can be especially important in contexts where incorrect information can have serious consequences, such as in financial or medical decision-making. Users who are interested in learning more about how AsscherAi can help their organisation can contact us for more information.
When hallucination is the wrong choice
There are certain contexts in which hallucination is the wrong choice, such as when the stakes are high and incorrect information can have serious consequences. In these cases, it is especially important to ensure that the system is providing accurate and reliable answers, and that it is not filling in gaps in the data with incorrect information. By using retrieval-based approaches and carefully tuning and testing the system, organisations can help to reduce the risk of hallucination and ensure that their users are getting the accurate information they need.
Frequently asked questions
What causes hallucination in AsscherAi query results
Hallucination occurs when the system fills gaps in data with incorrect information, resulting in answers not grounded in reality.
How can hallucination be reduced in AsscherAi
Retrieval-based approaches can help reduce hallucination by retrieving answers from a database of pre-existing information.
What is the difference between hallucination and inference
Inference uses actual data to make logical conclusions, whereas hallucination generates answers not supported by the data.
Why is it important to address hallucination in AsscherAi
Hallucination can lead to incorrect decisions, making it essential to identify and mitigate this issue to ensure accurate and reliable query results.