Stopping AI Hallucinations in Their Tracks

Understanding the causes and Appen's solutions

As large language models (LLMs) with generative AI applications become increasingly sophisticated, there is a growing concern about the potential for these models to produce inaccurate or misleading output.  This issue is known as “hallucination,” where AI models produce completely fabricated information that’s not accurate or true.  Hallucinations can have serious implications for a wide range of applications, including customer service, financial services, legal decision-making, and medical diagnosis.

Hallucination can occur when the AI model generates output that is not supported by any known facts. This can happen due to errors or inadequacies in the training data or biases in the model itself and large language models rarely say they do not know an answer. To mitigate this risk, researchers are exploring several approaches. One is to introduce more constraints on the model’s output, such as limiting the length of responses or requiring the model to stay within the realm of known facts. Another approach is to incorporate feedback from humans, as is done in RLHF, allowing them to flag and correct any errors or false information. Transparency in AI models is also an important factor, especially when it comes to decision-making processes. By making these processes more transparent, it becomes easier to identify and correct errors or biases that may lead to hallucination.

While these solutions are promising, they are by no means foolproof. As AI models become more complex and capable, it’s likely that new issues will emerge that require further research and development. By remaining vigilant and proactive in addressing these challenges, we can ensure the benefits of generative AI are realized while minimizing potential risks. 

As the field of AI continues to evolve, it will be essential for researchers, developers, and policymakers to work together to address emerging issues and ensure that these technologies are used in a responsible and beneficial way. In doing so, we can unlock the full potential of AI while eliminating the potential for harm. 

What Causes Hallucinations in AI Models 

There are several factors that can contribute to the development of hallucinations in AI models, including biased or insufficient training data, overfitting, limited contextual understanding, lack of domain knowledge, adversarial attacks, and model architecture. 

  • Biased or insufficient training data: AI models are only as good as the data they are trained on. If the training data is biased, incomplete, or insufficient, the AI model may develop hallucinations based on its limited understanding of the data it has access to. This is of particular concern in situations where the large language model has been trained using open internet data where bias and misinformation runs rampant.  
  • Overfitting: When an AI model is overfit to the training data, it may start to produce outputs that are too specific to the training data and do not generalize well to new data. This can lead to the model generating outputs that are hallucinations or irrelevant. 
  • Lack of contextual understanding: AI models that lack contextual understanding may produce outputs that are out of context or irrelevant. This can lead to the model generating outputs that are hallucinations or nonsensical. 
  • Limited domain knowledge: AI models that are designed for a specific domain or task may generate hallucinations when presented with inputs outside of their domain or task. This is because they may lack the necessary knowledge or context to generate relevant outputs. We see this when a model has limited understanding of different languages. Though a model may be trained on a vast set of vocabulary words in multiple languages, it may lack the cultural context, history and nuance to string concepts together correctly. 
  • Adversarial attacks: Unlike red teaming, where a team is assembled to “break” a model with the goal of improving it, AI models can also be susceptible to adversarial attacks. When malicious actors deliberately manipulate the inputs to the model, it can cause it to generate incorrect or malicious outputs. 
  • Model architecture: The architecture of the AI model can also impact its susceptibility to hallucinations. Models with more layers or parameters may be more prone to generating hallucinations due to the increased complexity. 

By addressing these primary causes of hallucinations, your AI models can be designed and trained to produce more accurate and relevant outputs, minimizing the risk of generating hallucinations. 

women looking at a laptop with a confused look, there is a red triangle with an exclamation point in the center and it says AI HALLUCINATIONS

Hallucinations Tackling, Powered by Appen 

Preventing hallucination in GPT AI models will require a multifaceted approach that incorporates a range of solutions and strategies. By continuing to explore new methods and technologies, researchers can help ensure that these powerful tools are used in a responsible and beneficial way. 

At Appen, we understand the importance of addressing the issue of hallucination in generative AI models. As a strategic AI partner offering data services, we have developed innovative solutions to help minimize the risk of hallucination and improve the accuracy and reliability of generative AI models. 

Diverse High-quality Training Data to Prevent Hallucinations in AI Models 

One of the key ways that Appen is addressing the issue of hallucination is by providing diverse and high-quality training data for AI models. By using a diverse range of training data, we can help ensure that AI models are exposed to a wide range of contexts and scenarios, which can help prevent the model from generating inaccurate or misleading output.

In addition to providing high-quality training data, Appen is also developing innovative solutions to help improve the context of decision-making processes in AI models. One of these solutions involves using natural language processing (NLP) techniques to analyze the context of a given input and provide additional information to the model.

For example, if a customer service chatbot receives a question from a user, we can improve the model efficiency by using different NLP technique such as Name Entity Recognition orf Sentiment analysis. These allows us to analyze the context of the question and provide additional information about the user’s history, preferences, and past interactions with the chatbot. The additional information can help the model generate more accurate and relevant responses, while also minimizing the risk of hallucination. 

The Importance of Human in the Loop in Tackling Hallucinations 

Another innovative solution that Appen offers to tackle hallucination in generative AI models is the use of reinforcement learning with human feedback (RLHF). RLHF involves developing a reward model based on human preferences and feedback that will be used to steer the language model toward more alignment, ie helpful, honest and harmless output.  

Imagine a healthcare organization that wants to develop an LLM to help diagnose and treat patients. They might use Appen’s human-in-the-loop system to train and validate their model. Human experts, such as doctors and nurses, would review the model’s output and provide feedback on whether it is accurate and relevant to the patient’s symptoms and medical history. This feedback would then be used to steer the model behavior toward more alignment and improve its accuracy, which may include for the model to learn to say that it cannot answer a question with certainty. Additionally, Appen’s team of linguists and language experts could provide context and domain knowledge to the model, helping it to better understand medical terminology and generate more relevant outputs. 

In addition to providing oversight, humans can also be used to provide feedback and corrective input to the model. This involves monitoring the output of the model and flagging any responses that are inaccurate or inappropriate, as well as providing corrective feedback to help the model learn and improve over time. 

By using human-in-the-loop for hallucinations tackling in the example above, the healthcare organization can develop a more accurate and reliable LLM that can assist medical professionals in diagnosing and treating patients. The model can also be continuously updated and refined based on new data and feedback, ensuring that it remains accurate and up-to-date. This can ultimately lead to improved patient outcomes and more efficient use of healthcare resources. 

Explainability and Interpretability 

Finally, Appen is also developing innovative solutions to improve the explainability and interpretability of AI models, which can help prevent hallucination and ensure that the output of the model is transparent and understandable.

For example, in a legal decision-making application, an AI model could be used to generate potential legal arguments or decisions based on historical case data. However, to ensure that the output of the model is transparent and understandable, the decision-making process of the model can be explained using natural language and visualizations, which can help human experts understand and evaluate the model’s output.

At Appen, we are committed to developing innovative solutions to help tackle the issue of hallucination in generative AI models. By providing high-quality training data, improving the context of decision-making processes, using reinforcement learning with human feedback, and improving the explainability and interpretability of AI models, we can ensure these powerful tools are used in a responsible and ethical way. With our expertise in AI and machine learning, Appen is well-positioned to empower businesses and organizations to leverage large language models while minimizing the risk of hallucination. As the field of AI continues to evolve, we will remain at the forefront of developing new and innovative solutions to address the challenges and opportunities of this exciting field. 

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