Hallucinations of LLMs: when datasets shape the reality of AI
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 the hallucination proves to be one of the most worrying. For example, ChatGPT faces significant challenges with hallucinations, sometimes producing incorrect information while appearing consistent and plausible.
But how do you define “hallucination” in artificial intelligence? While a hallucination is technically defined as a mathematical error, it's actually a fairly simple concept: LLM hallucination occurs when a model generates inaccurate or unsubstantiated information, thus giving the illusion of in-depth knowledge or understanding where there is none. This phenomenon highlights the complex challenges associated with training models, but also with building complete and complex datasets, and by extension with data annotation (i.e. the association of metadata or tags with unstructured data) - data used to train models.
Researchers are actively working to understand and mitigate these hallucinations (and especially to limit their impact in real applications of artificial intelligence), adopting a variety of approaches to improve models and reduce biases.
💡 By shaping the data used for learning, datasets and annotation directly influence accuracy and the reliability of the results produced by LLMs. In this article, we share a point of view on this subject with you!
What are the possible causes of LLM hallucinations?
The causes of hallucinations in LLMs (large language models) can be attributed to several factors mainly related to annotation errors in particular. They are expressed in answers that are inconsistent or factually incorrect. They are mainly the result of how a model is trained and its intrinsic limitations. Several studies are exploring the causes of LLM hallucinations, showing that these phenomena are unavoidable for any calculable LLM. Here are some of these causes:
- Insufficient or biased training data
LLMs are trained on vast sets of textual data 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.
- Excessive generalization
LLMs tend to generalize information based on 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 don't have an intrinsic understanding of the real world. They're just manipulating word sequences based on statistical probabilities. In the absence of adequate context, they can generate feedback that seems logical but is 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 can then fill in the gaps with invented information, resulting in hallucinations.
- Model memory capacity limits
LLMs have limits on the amount of information they can process and retain at the same time. When dealing with complex or time-consuming information, they can lose critical details, leading to inconsistent or incorrect responses (but with all the confidence in the world!).
- Alignment issues
LLMs are not always perfectly aligned with the intentions of their users or the goals for which they are deployed. This disconnection may cause inappropriate or incorrect responses.
- Influence of pre-existing models
LLMs can be influenced by one (or more) pre-existing linguistic models 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 risks of hallucinations.
How do datasets and data annotation affect the performance of natural language models?
LLMs rely on massive data sets to learn how to generate text in a consistent and relevant manner. However, the quality, precision and relevance of these annotations directly determine the performance of the model. Below are the two main aspects of an artificial intelligence product influenced by the datasets used to train the models:
Consistency of responses
When data is annotated rigorously, the model can make more accurate connections between inputs and outputs, improving its ability to generate consistent and accurate responses.
Conversely, errors or inconsistencies in the annotation can introduce biases, ambiguities, or incorrect information, which can cause the model to produce erroneous results, or even to “hallucinate” information that is not present in the training data.
Ability to generalize
The influence of data annotation is also evident in the model's ability to generalize from the examples it saw during training. High-quality annotation helps the model understand the nuances of language, while poor annotation can limit this ability, leading to poor performance, especially in contexts where accuracy is critical.
What are the impacts of LLM hallucinations on real applications of Artificial Intelligence?
LLM hallucinations can seriously compromise the reliability of the AI applications in which these models are integrated. When LLMs generate incorrect or unsubstantiated information, it can lead to serious errors in automated or AI-assisted decisions.
This is particularly true in sensitive areas such as health, finance or law. A loss of reliability can reduce user confidence in these technologies, limiting their adoption and usefulness.
Health consequences
In the medical field, for example, LLM hallucinations can lead to misdiagnoses or inappropriate treatment recommendations.
If a LLM model generates medical information that seems plausible but is incorrect, this could have serious consequences on patients' health, or even put their lives in danger. The adoption of these technologies in the health sector therefore depends heavily on the ability to minimize these risks.
Risks in the financial sector
In the financial sector, LLM hallucinations can lead to erroneous decision-making based on inaccurate information. This could result in poor investment strategies, incorrect risk assessments, leaks related to data security issues, or even fraud.
Financial institutions must therefore be particularly vigilant about the use of LLMs and ensure that the data used by these models is reliable and properly annotated. This is the reason why this industry is particularly prolific from a regulatory point of view!
Ethical and legal issues
LLM hallucinations also raise ethical and legal questions. For example, if an LLM model generates defamatory or deceptive information, this may result in legal proceedings for defamation or for spreading false information.
Additionally, the ability of LLMs to generate hallucinations poses challenges in terms of transparency and accountability, especially in contexts where automated decisions can have a direct impact on individuals.
Impacts on the 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. Additionally, it can lead to increased frustration among users, who may be misled by wrong answers.
Influence on business reputation
Businesses deploying AI applications based on LLMs should also be aware of any potential impact on their reputation. If an LLM model used by a business starts generating frequent hallucinations, it can damage brand image and reduce customer trust.
💡 The proactive management of these risks is therefore essential to maintain a positive reputation and to ensure the sustainability of the company in an increasingly competitive market.
How to detect hallucinations in LLMs?
Detecting hallucinations in Large Language Models (LLM) is a complex challenge due to the very nature of hallucinations, which involve the content generation plausible but incorrect or unfounded. However, there are several approaches that can be used to identify these errors.
Use of cross-check models
One method is to use multiple LLM models to check the responses generated. If different models produce different answers to 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
An LLM hallucination can be detected by comparing LLM responses with reliable and well-established databases or knowledge sources. Hallucinations can be detected when the responses generated by the model contradict these sources. This method is particularly useful in areas where specific facts are needed, such as medicine or law.
Analysis of trust models
LLM models can also be equipped with internal mechanisms to assess trust for each response they produce. Responses generated with low confidence may be suspicious and require further verification. This makes it possible to specifically target model outputs that are more likely to be hallucinations.
How do you correct hallucinations in LLMs?
Once hallucinations are detected, several strategies can be put in place to correct or minimize their occurrence.
Improving data annotation and datasets
As mentioned earlier, 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 reviews of annotated data sets by experts 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 mistakes and appropriate corrections, the model can learn to avoid these types of drifts in the future. This method of learning through correction is an effective way to improve model performance.
Incorporation of validation rules
Incorporating 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 outputs before they are presented to the end user.
Conclusion
LLM hallucinations represent a major challenge for the reliability and effectiveness of artificial intelligence applications. By focusing on a data annotation rigorously and on the continuous improvement of models, it is possible to reduce these errors and ensure that LLMs provide 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!
