Language models, such as Large Language Models (LLM), are playing an increasingly central role in artificial intelligence (AI) applications. However, these models are not free of limitations, among which hallucination is proving to be one of the most worrying. For example, ChatGPT encounters significant challenges with hallucinations, sometimes producing incorrect information while appearing coherent and plausible.
But how do you define "hallucination" in artificial intelligence? While a hallucination is technically defined by a mathematical error, it's actually a fairly simple concept: LLM hallucination occurs when a model generates inaccurate or unsubstantiated information, giving the illusion of in-depth knowledge or understanding where none exists. This phenomenon highlights the complex challenges associated not only with model training, but also with the constitution of complete and complex datasets, and by extension with data annotation (i.e. the association of metadata or tags with unstructured data) - data used for model training.
Researchers are actively working to understand and mitigate these hallucinations (and especially to limit their impact in real-world applications of artificial intelligence), adopting various approaches to improve models and reduce biases.
💡 By shaping the data used for learning, datasets and annotation directly influence the accuracy and reliability of the results produced by LLMs. In this article, we share a point of view on this topic!
What are the possible causes of LLM hallucination?
The causes of hallucinations in LLMs (large language models) can be attributed to several factors, mainly linked to annotation errors. They manifest as incoherent or factually incorrect responses. They result mainly from the way a model is trained and its intrinsic limitations. Several studies explore the causes of LLM hallucinations, showing that these phenomena are inevitable for any computable LLM. Here are just a few of the causes:
- Insufficient or biased training data
LLMs are trained on large text datasets from the Internet and other sources. If this training data contains incorrect, biased or inconsistent information, the model can learn and reproduce these errors, leading to hallucinations.
- Over-generalization
LLMs tend to generalize information from training data. Sometimes, this generalization can go too far, resulting in the generation of plausible but incorrect content. This incorrect extrapolation is a form of "hallucination".
- Lack of context or understanding of the real world
LLMs have no intrinsic understanding of the real world. They simply manipulate sequences of words based on statistical probabilities. In the absence of proper context, they can generate returns that seem logical but are disconnected from reality.
- Complexity of the questions asked
Complex or ambiguous questions or prompts may exceed the model's ability to provide correct answers. The model may then fill in the gaps with invented information, resulting in hallucinations.
- Model memory capacity limits
LLMs have limits to the amount of information they can process and retain at the same time. When they have to manage complex or lengthy information, they can lose essential details, leading to inconsistent or incorrect replies (but with all the confidence in the world!).
- Alignment problems
LLMs are not always perfectly aligned with their users' intentions or the purposes for which they are deployed. This disconnect can lead to inappropriate or incorrect responses.
- Influence of pre-existing models
LLMs may be influenced by one (or more) pre-existing linguistic patterns and common sentence structures in the training data. This can lead to systematic biases in responses, including hallucinations.
💡 Understanding these causes is essential for improving the reliability and accuracy of LLMs, as well as for developing techniques to mitigate the risk of hallucinations.
How do datasets and data annotation influence the performance of natural language models?
LLMs rely on massive datasets to learn how to generate text in a consistent and relevant way. However, the quality, accuracy and relevance of these annotations directly determine the model's performance. Below are the two main aspects of an artificial intelligence product influenced by the datasets used to train the models:
Consistency of answers
When data are rigorously annotated, the model can establish more precise links between inputs and outputs, improving its ability to generate consistent and accurate responses.
Conversely, errors or inconsistencies in the annotation can introduce bias, ambiguity or incorrect information, leading the model to produce erroneous results, or even to "hallucinate" information that is not present in the training data.
Generalization capability
The influence of data annotation can also be seen in the model's ability to generalize from the examples it has seen during training. High-quality annotation helps the model to understand the nuances of language, while poor annotation can limit this ability, leading to degraded performance, particularly in contexts where accuracy is crucial.
What impact do LLM hallucinations have on the real applications of Artificial Intelligence?
LLM hallucinations can seriously compromise the reliability of AI applications in which these models are integrated. When LLMs generate incorrect or unsubstantiated information, this can lead to serious errors in automated or AI-assisted decisions.
This is particularly true in sensitive areas such as healthcare, finance and law. A loss of reliability can reduce users' confidence in these technologies, limiting their adoption and usefulness.
Consequences for the health sector
In the medical field, for example, LLM hallucinations can lead to misdiagnoses or inappropriate treatment recommendations.
If an LLM model model generates medical information that seems plausible but is incorrect, this could have serious, even life-threatening, consequences for patients' health. The adoption of these technologies in the healthcare sector is therefore highly dependent on the ability to minimize these risks.
Risks in the financial sector
In the financial sector, LLM hallucinations can lead to faulty decision-making based on inaccurate information. This could result in poor investment strategies, incorrect risk assessments, data security leaks or even fraud.
Financial institutions must therefore be particularly vigilant when using LLMs, and ensure that the data used by these models is reliable and correctly annotated. That's why this industry is particularly prolific from a regulatory point of view!
Ethical and legal issues
LLM hallucinations also raise ethical and legal issues. For example, if an LLM model generates defamatory or misleading information, this could lead to legal action for defamation or dissemination of false information.
Moreover, the ability of LLMs to generate hallucinations poses challenges in terms of transparency and accountability, particularly in contexts where automated decisions can have a direct impact on individuals.
Impact on user experience
Hallucinations can also degrade the user experience in more common applications, such as virtual assistants or chatbots. If these systems provide incorrect or inconsistent information, users can quickly lose confidence and stop using these technologies. What's more, this can lead to increased frustration among users, who may be misled by incorrect responses.
Influence on corporate reputation
Companies deploying LLM-based AI applications also need to be aware of any potential impact on their reputation. If an LLM model used by a company starts to generate frequent hallucinations, this can damage the brand's image and reduce customer trust.
💡 Proactive management of these risks is therefore essential to maintaining a positive reputation and ensuring the company's longevity in an increasingly competitive market.
How to detect hallucinations in LLM?
Detecting hallucinations in large language models (LLMs) is a complex challenge due to the very nature of hallucinations, which involve the generation of plausible but incorrect or unsubstantiated content. However, several approaches can be used to identify these errors.
Using cross-checking models
One method is to use several LLM models to check the answers generated. If different models produce divergent responses for the same question or context, this may indicate the presence of a hallucination. This approach is based on the idea that hallucinations are less likely to be consistent across different models.
Comparison with reliable sources of knowledge
LLM hallucination can be detected by comparing LLM responses with reliable, well-established databases or knowledge sources. Hallucinations can be detected when model-generated responses contradict these sources. This method is particularly useful in fields where precise facts are required, such as medicine or law.
Analysis of confidence models
LLM models can also be equipped with internal confidence assessment mechanisms for each response they generate. Responses generated with low confidence may be suspect and require further verification. This makes it possible to specifically target model outputs that are more likely to be hallucinations.
How to correct hallucinations in LLM?
Once hallucinations have been detected, several strategies can be put in place to correct or minimize their appearance.
Enhanced data annotation and datasets
As mentioned above, the quality of data annotation is critical. Improving this quality, by ensuring that annotations are accurate, consistent and comprehensive, can reduce the likelihood of generating hallucinations. Regular expert reviews of annotated datasets are also essential.
Fine-tuning the model with correction data
The hallucinations identified can be used to refine the model. By providing the LLM with examples of its errors and appropriate corrections, the model can learn to avoid these types of drifts in the future. This learning-by-correction method is an effective way of improving model performance.
Incorporation of validation rules
The integration of specific validation rules, which check the plausibility of responses based on context or known facts, can also limit hallucinations. These rules can be programmed to intercept and review output before it is presented to the end-user.
Conclusion
LLM hallucinations represent a major challenge for the reliability and efficiency of artificial intelligence applications. By focusing on data annotation and continuous model improvement, it is possible to reduce these errors and ensure that LLMs deliver more accurate and reliable results.
As AI applications continue to develop, it is extremely important to recognize and mitigate the risks associated with hallucinations to ensure sustainable and responsible benefits for businesses in all sectors!
