The global LLM market is projected to reach its golden period, attaining a CAGR of 79-80% between 2023 and 2030. Studies reveal that LLM growth is expected to rise from $1,590 million in 2023 to $259,8 million in 2030. As the backbone of generative AI, LLMs have stormed the AI world, and the skies haven’t cleared since. But one persistent issue that keeps lingering in and around LLMs is hallucinations. If you are an AI enthusiast or someone who loves to stay updated on technology, you might have heard the word hallucinations ringing in your ears. 

Hallucinations are the LLM’s ability to fabricate responses when unsure about the subject matter. Such hallucinations may erode the trust factor and reliability of the LLM models. These can also be very harmful and devastating, especially in industries where accuracy and data reliability are non-negotiable. 

So why do these hallucinations occur in the first place?

When LLMs are queried on topics outside their knowledge base, they generate beautifully crafted, cohesive responses that may or may not be accurate. For LLMs, anything within their knowledge spectrum is their forte; beyond that, they try to be probabilistic rather than deterministic.

If (LLMs) occasionally sound like they have no idea what they’re saying, it’s because they don’t. LLMs know how words relate statistically, but not what they mean.

explains the IBM research blog.

Now addressing the actual question, why?

The very reason for the occurrence of hallucinations falls under four main buckets.

Unclear knowledge scope

The most common reason is that the training data is noisy or oversaturated and does not match the model’s knowledge scope. Like too many cooks spoil the broth, having outliers within the training data can lead to poor pattern recognition. This situation can hurt the further steps in natural language processing, such as classification and prediction, reproducing responses with factual errors.

Poor data quality

Data quality is very important for LLMs. If that becomes questionable, it can lead to problems like misclassification, mislabelling, and becoming the culprit for the most dangerous AI crime, bias. In data automation and processing pipelines, poor input data can disrupt the entire system, leading to inaccurate classifications, faulty extractions, and unreliable outputs across enterprise systems. Let me help you understand how lousy data can affect the output quality. 

Let’s assume an apparel brand uses an AI-driven product catalog to elevate customer experience. The company predominantly uses LLM-based automation to manage their product listings.

Imagine a case wherein a sweater is mislabelled as a summer tee, now let us see how this misalignment can cause or affect the overall process. For instance, if a customer is exploring options under the category winter essentials, they might not be able to find that particular sweater; instead, this sweater will be suggested to customers looking for summer t-shirts.

Suppose a chatbot is employed to address customer FAQs, and a customer enquires about this faulty labelled sweater, asking about the sweater’s capability to keep warm. In that case, the bot may fetch something like the product is best suited for a tropical climate. Either of these situations can frustrate the customer and lead to customer dissatisfaction.

Imagine this in less forgiving industries like healthcare, legal, and finance, and the result can be detrimental. If this same issue occurs in patient records, legal documents, and financial statements, it might lead to costly mistakes, eroding customer trust and brand reputation.

Data sparsity

Another reason is the scantiness of the training dataset, which can depend on factors such as specificity, how often data is refreshed, etc. Since LLMs are fed on massive datasets that comprise a vast knowledge spectrum, they stumble when they are questioned on particular domains or niche topics. Also, not checking LLMs for ambiguity and relevance in real-time can cause the LLMs to generate responses that may generate vague or even contradictory answers.

Contextual guessing

Since the core technology powering LLMs is natural language processing, it is too good at predicting patterns from word constellations and phrases. So when it encounters a less familiar search query, it does the guesswork in generating the next word based on the previous word configurations, which may be factually incorrect.

So what is the fix?

According to studies carried out on LLMs, there are a few processes that are said to improve reliability and yield fewer hallucinations.

Finetuning

One of the most common ways of handling hallucinations is finetuning the LLMs with targeted datasets. Fine-tuning over a period of time can make LLMs perform better with domain-specific queries. In automated data processing workflows, this ensures that LLMs learn from structured patterns and can make more reliable contextual inferences, reducing the risk of bad data propagation.

Architectural modifications

AI experts say that changing the underlying architecture that runs the LLMs, such as adding memory modules or even incorporating reasoning capability within the architecture, can help overcome hallucinations.

Decoding strategies

Modifying the decoding algorithms LLMs use also helps mitigate hallucinations. Techniques like top-p truncation and diverse beam search can improve the accuracy of LLM responses.

Hallucination detection

Another prescribed method might be incorporating techniques that can automatically detect hallucinations from the output results. This can be done by infusing techniques like anomaly detection, checking for inconsistency, or even employing separate models for hallucination inspection.

Prompt engineering

Prompt engineering is widespread in the AI world. LLMs can increase the probability of generating factually plausible responses by giving a descriptive prompt with detailed contextual information, specifying additional instructions, and, most importantly, mentioning constraints.

Retrieval augmented generation

Finally, but importantly, the RAG architecture is the best-known method for overcoming AI hallucinations. It is a synergy of the generative capability of LLMs and continuous retrieval capability. This technique is noteworthy because it eliminates the need for frequent training models and updates the LLM parameters, making it a cost-effective solution and avoiding computational and financial costs.

Why is retrieval augmented generation the best solution?

RAG architecture connects LLMs with external databases that include relevant and up-to-date proprietary data, ensuring LLMs don’t hallucinate. This real-time retrieval mechanism is crucial for automated data processing pipelines, where real-time accuracy is essential for powering decision intelligence, customer-facing systems, and backend operations.

By incorporating RAG, users can access the model’s data sources, allowing them to cross-check facts and gain a competitive edge in accuracy. This is precisely why RAG stands out as the ultimate safeguard against hallucinations, empowering systems to deliver consistent, trustworthy results.

It’s the difference between an open-book and a closed-book exam,” Lastras said. “In a RAG system, you are asking the model to respond to a question by browsing through the content in a book, as opposed to trying to remember facts from memory.

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Some best practices to adopt while implementing RAG

Leveraging structured data for retrieval

Having structured data as a retrieval source reduces the chances of ambiguity, making way for reliable responses grounded in facts.

Contextualise retrieval results

Even after retrieval, it is essential to have a feedback loop to understand how well the AI model has captured the context for the user query. To tick this box, the retrieved data should be checked against multiple aspects of the user intent behind the query. Having this practice can reduce the possibility of hallucinations. 

Improving model interpretation

Another way to weed out hallucinations is by adding interpretability techniques that help us understand the underlying logic behind generating a particular response. This can be achieved by employing attention mechanisms or even interpretability tools that can track every step the model takes, allowing users to witness a transparent process. A transparent decision-making process can help us identify the root cause of hallucinations and, hence, can help treat them as soon as possible.

Wrapping up,

Retrieval augmented generation is, so far, the best-known technique for harnessing the potential of LLMs. At Xtract.io, our intuitive RAG feature, XRAG, brings out the best of this architecture. Incorporating XRAG architecture into your system can offer multiple benefits by generating responses that are accurate, most contextually relevant, and up-to-date. Whether it’s streamlining unstructured data processing or scaling data automation tasks across teams, XRAG empowers enterprises to extract more value with less manual intervention, giving businesses an upper hand in intelligence and efficiency.

Though RAG is a better alternative to overcome hallucinations, like any other technique, it is not the ultimate cure. It comes with limitations like its reliance on the quality of data. Thereby, incorporating human oversight and expert knowledge is imperative to make the most out of the RAG technique. While RAG might not be the life-changing solution, it can be a better alternative if we have the quality data and the right people with expert knowledge. On an ending note, we all know that the AI world replenishes itself daily, so will RAG continue to top the list, or will there be a better solution to overtake RAG? To answer the question, let us wait and watch what comes next.