Recent breakthroughs in modern technology, like generative AI, can unlock innovation and creativity on a massive scale. However, as transformative as GenAI can be, it also comes with its own set of challenges that could get in the way of its widespread adoption.
As AI models grow, so too can its complexity–and therefore introducing concerns such as AI “hallucinations,” which refers to inaccurate fabrication of content based on input data. There are certainnly challenges to using GenAI, but also ways to reduce AI hallucinations.
The Potential Limitations of Large Language Models
Large Language Models (LLMs) are inherently characterized by their probabilistic and non-deterministic nature. LLMs produce content based on the input provided and by assessing the probability of a specific sequence of words occurring next. What LLMs lack is the concept of knowledge and instead depend entirely on traversing their trained dataset much like a recommendation system. The text or content generated often appears grammatically correct, but the output primarily aims to meet statistical patterns found in the given input or provided prompt.
The probabilistic nature of LLMs can be a double-edged sword. When the objective is to provide accurate answers, such as in the case of improving search engines or making critical decisions based on responses, the occurrence of hallucinations is detrimental and potentially harmful.
However, in creative pursuits, this same characteristic can be harnessed to nurture artistic creativity, enabling the rapid generation of art, storylines and scripts. In either instance, the inability to trust the model’s output can have its own set of consequences. It undermines the confidence in these systems and significantly diminishes the true impact AI can have to enhance human productivity and foster innovation.
AI models are only as effective and intelligent as the dataset they’ve been trained on. Often, AI hallucinations can occur and are a result of a variety of factors, including overfitting, data quality and data sparsity:
- Overfitting can cause AI models to learn and be trained on low-quality pattern recognition which can lead to inaccuracies and errors. AI model complexity and noisy training data causes overfitting in LLMs.
- Data quality can contribute to the mislabeling and incorrect categorization of data. For example, say a photo of a goldfish is mislabeled as a great white shark in a training data set. When an LLM is queried down the line about goldfish facts, it could generate a response such as, “Goldfish have seven rows of teeth and grow to about 20 feet in length.” Additionally, AI models that lack relevant data or pick up biases when generating decisions can spread misinformation.
- Data sparsity occurs when a dataset has missing values, which is one of the most common challenges that can lead to AI hallucinations. When an AI system is left to fill in the gaps on its own, inaccurate conclusions can be made since it lacks judgment and critical thinking.
How to Combat AI Hallucinations
Fortunately, there are proven techniques to mitigate AI hallucinations in LLMs. Approaches such as fine-tuning and prompt engineering can help address potential shortcomings or biases in AI models. And arguably, the most important technique of all, retrieval-augmented generation (RAG) can help to ground LLMs with contextual data and can reduce hallucinations and improve the accuracy of AI models with up to date data.
- Fine-tuning, also known as retraining, the model helps accurately generate content that is relevant to the domain. This technique may take longer when it comes to mitigating hallucinations. Additionally, the data can become outdated if it is not trained continuously. While it can help to combat hallucinations, the downside can be that it frequently comes with a significant cost burden.
- Prompt engineering gives AI models additional context which can lead to fewer instances of hallucinations. This technique helps LLMs produce more accurate results because it feeds models highly descriptive prompts.
- RAG is one of the most promising techniques to alleviate AI hallucinations because of its focus on feeding LLMs the most accurate, up-to-date data. It’s an AI framework that pulls data from external sources to gather context to improve LLM responses.
The Importance of RAG and Real-Time Data to Reduce AI Hallucinations
RAG has a wide range of applications in the field of generative AI and Natural Language Processing (NLP). They are particularly effective in tasks that require a deep understanding of context and the ability to reference multiple sources of information. RAG is often used in applications, such as virtual assistants, chatbots, text summarization and contextual content creation, that are meant to generate precise and relevant responses. That’s why real-time data is crucial when it comes to RAG because it helps create models with proprietary and contextual data to enhance the quality and accuracy of AI-generated responses — which is key for reducing AI hallucinations and the spread of misinformation.
For example, a large retailer uses an AI chatbot for customer service. When a customer enters a question about a product, the chatbot provides a response by using RAG, pulling in pre-trained knowledge, as well as retrieving up-to-date information about the product and the user’s profile to generate relevant and up-to-date content tailored to the user’s history or purchase patterns from the retailer’s database. By doing this, it ensures data is current and accurate to formulate a precise response for the customer.
To further enhance RAG’s impact on mitigating AI hallucinations, it must be paired with an operational data store for storing data in high-dimensional mathematical vectors. The data store can then turn the model’s query to a numerical vector. As a result, the vector database gains the capability to query for relevant papers or passages, regardless of whether they include the same terms. An AI model’s access to real-time data can also support dynamic learning and adaptation. AI models can then regularly update their understanding of topics, mitigating the chances of generating hallucinations based on outdated or static information.
About the author: Rahul Pradhan is the vice president of product and strategy at Couchbase, provider of a leading modern database for enterprise applications that 30% of the Fortune 100 depend on. Rahul has over 20 years of experience leading and managing both Engineering and Product teams focusing on databases, storage, networking, and security technologies in the cloud.