Large language models (LLMs) have made a massive splash in recent years, known for their ability to interpret and generate content that mimics human communications. As a subset of machine learning, LLMs facilitate sophisticated human-to-bot interactions in the contact center ecosystem. With the rise of self-service CX channels and evolution of customer expectations, businesses can use LLM-based bots to forge stronger customer relationships, improve efficiency, and reduce churn.
Cyara Botium helps leading global brands realize the true value of their AI investments throughout the entire bot development lifecycle.
In the best-case scenario, LLMs deliver personalized, cost-effective, and natural interactions that make your customers feel as though they are speaking with human agents. However, it’s common to see the pitfalls of integrating LLM-powered bots into your infrastructure. Without the right oversight, these bots can open the door for disaster. From spreading misinformation, generating biased and harmful answers, or responding to your customers’ queries with nonsense, LLM-powered CX channels pose a wide range of reputational, compliance, and financial risks.
A Brief Overview of LLM Hallucinations
LLM hallucinations occur when your bot generates a response that is factually inaccurate, nonsensical, or inconsistent. For developers, hallucinations make it difficult to identify and remediate CX defects within generative AI-based systems. Meanwhile, for your customers, LLM hallucinations can be confusing, frustrating, or even harmful, damaging your brand’s reputation and putting your long-term success on the line.
Within the contact center space, your team works to train your bot to understand customer input and generate responses that meet your customers’ needs. For example, an LLM-powered chatbot can help a customer check their order status, offer personalized product recommendations based on the customer’s past shopping trends, translate customer queries in real time, and more.
However, a hallucination undoes all the hard work you put into developing, training, and deploying your bot. For example, your brand may extend its global reach with an LLM-powered bot that translates English to German. During an interaction, the bot suffers a hallucination. Instead of accurately translating, “I want to start a return,” it instead translates a phrase that reads, “I want a sandwich.” Instead of making the interaction seamless, the bot’s hallucination creates confusion and frustration for the customer, who is more likely to seek out a competitor in the future.
In this example, the LLM’s hallucination causes some minor confusion for your customer. While your business may suffer reputational or financial consequences from losing this customer, this is just one brief instance. In the real world, LLM hallucinations have already led to disastrous consequences. While the retail industry might view a chatbot spreading misinformation as an annoyance, the stakes rise when LLM-based bots penetrate the financial, healthcare, legal, and government sectors, for example. Businesses in these industries must adhere to strict regulatory standards, and customers seeking help need access to reliable, accurate, and trustworthy information, without the fear that a chatbot is biased or generating nonsensical answers to their questions.
While LLMs can help you transform the way you connect with your customers, it’s imperative to understand what causes hallucinations, and the steps you can take to mitigate the risk, before your brand suffers the consequences.
What Causes Hallucinations?
Unfortunately, there isn’t a single root cause for hallucinations, and they can emerge for a variety of reasons during development and deployment. Several causes for LLM hallucinations include:
Data Quality:
LLMs rely on a large amount of data to understand patterns and generate content. As your systems use this data to generate responses, they may pick up inaccuracies or biases in the training data’s content. For example, data that’s outdated, incomplete, or inaccurate can lead to gaps in the model’s understanding.
Lack of Common Sense:
Unlike humans, LLMs don’t have any real-world experiences or common sense. This can limit your bot’s ability to understand customer intent, lead to errors in content generation, and compromise your system’s ability to interpret vague queries.
Overfitting and Underfitting:
If you build a model that is too specific or too general to your training data, overfitting or underfitting occurs. When your bot suffers from overfitting, it will have difficulty handling new tasks, whereas underfitting causes your bot to generate nonsensical responses.
Vague Queries:
While LLMs are trained to understand customer inputs, vague or non-specific queries may cause hallucinations. In the cause of an ambiguous prompt, the system may generate a response based on a possible interpretation, which is not always accurate.
Because hallucinations aren’t derived from a specific stage during the development lifecycle, it’s critical to continuously test, monitor, and optimize your bot from the earliest stages of design, all through deployment and in the live environment. All it takes is a single hallucination to tarnish your brand’s reputation, put you at risk of compliance penalties, or threaten your bottom line.
Overcome LLM-Related Risks with Cyara
AI-powered CX channels have grown in prominence over the past several years, making it possible for businesses to deliver quick, efficient, cost-effective, and personalized customer interactions. But without the proper guardrails in place to oversee CX performance, your business can be subject to significant reputational, financial, and regulatory risks. But Cyara is here to help you optimize your AI-powered channels.
Cyara Botium is the world’s only true end-to-end conversational AI optimization platform, designed to help leading brands develop and deploy reliable AI-powered bots faster and with greater confidence. With Botium’s comprehensive testing, monitoring, and optimization tools, you can assure bot performance, mitigate risk, and validate your system’s scalability, so you can deliver quality interactions that will meet your customers’ expectations, without any additional obstacles.
Today, 90% of AI-powered projects are stuck in proof of concept, held back due to reputational and financial risks. But with our conversational AI testing suite, Cyara AI Trust in Botium, you can overcome the risk of LLM hallucinations to ensure your bots deliver reliable, trustworthy, and accurate responses.
There are many risks that put your business’ success in jeopardy. But with Cyara AI Trust, you can:
- Ensure your bots are optimized prior to deployment.
- Eliminate the risk of costly delays and rework.
- Identify and remediate potential risks before they affect your customers.
- Protect your business’ reputation.
- Improve your customers’ trust in your brand.
For many businesses looking to deploy LLM-powered bots, hallucinations can feel like an impossible hurdle, where they must choose to either settle for less sophisticated CX channels, or fall victim to costly risks. But this simply isn’t the case. By leveraging Cyara’s continuous, automated CX testing and monitoring solutions, you can detect hallucinations and take proactive measures to protect your customers from the spread of misinformation and biases.
Don’t wait for your bots to damage your business. Contact us to schedule a personalized demo today, or visit cyara.com to learn how we can help you assure CX performance.