Keeping LLMs accurate: Your guide to reducing hallucinations

Published on

Nov 2024

Over the last few years, Large Language Models (LLMs) have become accessible and transformative tools, powering everything from customer support and content generation to complex, industry-specific applications in healthcare, education, and finance.

However, as versatile as LLMs are, they have their limitations. For example, small context windows can limit long-form accuracy and a training bias toward English-language data restricts their effectiveness in multilingual scenarios. But one of the most pressing challenges today is hallucinations.

Let’s explore what hallucinations are, their types and causes, and actionable tips to prevent them.

What are LLM hallucinations?

LLM hallucinations refer to instances where a language model produces responses that sound plausible but are factually incorrect or entirely fictional. These hallucinations typically happen when the model lacks sufficient knowledge or context, causing it to fabricate information to fill perceived gaps. LLMs are designed to generate coherent text, so they will often respond with high-confidence statements, which can make even false outputs seem reliable.

In some cases, a hallucination might not be a big deal. For example, a person casually using a public LLM could ask for a list of fun facts and receive a fictional anecdote. While misleading, it’s a relatively low-stakes error.

However, hallucinations can be problematic when they affect users relying on LLM-powered products for accurate information. Imagine a customer using an LLM-integrated platform to handle complex financial transactions or receive medical guidance. A hallucination in these scenarios, such as incorrect advice on tax compliance or an inaccurate interpretation of medical symptoms, could lead to serious consequences.

The bottom line: addressing hallucinations is critical to advancing LLM applications and ensuring their reliability for end users.

Types of LLM hallucinations

Most of us have encountered a hallucination in our own experience with LLMs. But, as we’ve said, the impact of these hallucinations varies widely, with some being relatively harmless and others posing real risks.

Factual incorrectness: This type of hallucination involves misrepresenting factual information, such as presenting outdated business data or incorrect technical specifications. For instance, an LLM might provide a wrong answer about a software feature in a customer support chat. While minor errors may only inconvenience users, they can also damage trust if the model consistently provides misleading or outdated details in professional settings.
Misinterpretation and contextual errors: Misinterpretations occur when a model fails to understand the nuances of a user’s input or misclassifies information from its knowledge base. In the context of agent assistance or sales enablement tools, a model might misunderstand a customer's query or incorrectly classify a lead, resulting in unhelpful or irrelevant responses. Such errors can impact customer satisfaction, hinder sales, or cause inefficiencies when the model misinterprets crucial context.‍
Complete fabrications: In this case, the model generates entirely fictional content. For example, a public-facing LLM generating false statistics or inventing legal precedents can mislead users and create real-world risks that can have serious societal consequences, such as spreading misinformation, creating legal risks, undermining public health, eroding trust in AI systems, and amplifying biases that perpetuate social divisions.

What causes hallucinations?

There are dozens of variables that can contribute to LLM hallucinations, from prompt phrasing and temperature settings to fine-tuning choices and knowledge cutoffs. But we’re just going to focus on three primary factors that have a particularly strong impact on the likelihood of hallucinations.

Limitations in training data: LLMs can only generate responses based on the data available during their training. Consequently, when new information arises or specialized knowledge is required, models may rely on incomplete data or “guesswork,” leading to hallucinations. Even the most advanced models, without access to real-time data updates, can produce errors when handling current events or niche topics.
Model architecture constraints: One inherent limitation of LLMs is their constrained context windows, which restrict the amount of information they can process at once. This makes it challenging for models to handle extended conversations or follow complex narratives, leading to errors in later parts of the dialogue. Additionally, without a memory component, LLMs cannot retain information from previous interactions, increasing the likelihood of hallucinations in multi-step queries.‍
Overfitting and bias amplification: Overfitting to specific data subsets or sources can cause LLMs to repeat certain patterns or assumptions. This can result in bias amplification, where hallucinations are not only incorrect but are skewed by social or cultural biases in the training data. For example, a model trained on limited perspectives might generate outputs that reinforce stereotypes or favor particular viewpoints, complicating its use in sensitive applications.

Tips to prevent hallucinations

While hallucinations remain a challenge, several strategies can help reduce their frequency and impact.

Select appropriate models and training data

Not all LLMs are created equal, and choosing between open-source and proprietary models is a critical decision. Proprietary models, like OpenAI’s GPT-4 or Google’s PaLM, are often easier to deploy through APIs and may offer higher performance and support, though they come with licensing fees that can scale quickly with usage. Open-source models like Falcon LLM, StarCoder, and Meta’s LLaMA offer flexibility, transparency, and cost savings on licensing but require more in-house expertise and infrastructure costs to host and fine-tune effectively.

For certain applications—especially in fields like healthcare or finance—selecting a model trained on relevant, high-quality data can be essential for reducing hallucinations. Smaller, domain-specific models or well-maintained proprietary options may perform better than broad, general-purpose models in these cases, as they can be tailored to provide more reliable outputs for specialized needs.

Implement retrieval-augmented Generation (RAG)

Retrieval-augmented generation (RAG) is a powerful approach to ensure LLM responses are accurate and grounded in real-time, trusted information. By integrating retrieval mechanisms that cross-reference relevant data as responses are generated, RAG minimizes hallucinations and enhances the reliability of outputs. This technique can be especially useful in dynamic use cases, such as real-time transcription, where dependable information is crucial.

Gladia’s own LLM solutions integrate RAG to enhance response accuracy and mitigate the risks of hallucinations for customers across a wide range of applications.

Monitor and mitigate with human oversight

Deploying LLMs isn’t a “one-and-done” exercise; it requires ongoing human oversight and specialized expertise to keep outputs reliable. High-impact use cases need regular auditing to check for consistency and accuracy. This often involves roles like data scientists to analyze and fine-tune model performance, prompt engineers to craft optimized prompts, and compliance experts to ensure adherence to industry standards.

User feedback is also invaluable, as it enables teams to dynamically adjust model responses and refine LLM behavior based on real-world usage. This constant monitoring and feedback loop helps LLMs evolve to meet specific reliability standards and deliver dependable results over time.

Wrap-up

Mitigating and reducing hallucinations in LLMs begins with selecting the right model and training it on a diverse and expansive dataset. Models, even smaller ones, perform more reliably when exposed to a broad range of information during training, making them less prone to generating fabricated or inaccurate responses.

Another effective approach is using techniques like RAG, which not only integrates real-time retrieval mechanisms to ground the model's responses in factual data but can also validate the truthfulness of its output.

Finally, LLMs require ongoing human oversight, specialized expertise, and user feedback to keep outputs reliable.

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About Gladia

Gladia provides a speech-to-text and audio intelligence API for building virtual meetings, note-taking apps, call center platforms and media products, providing transcription, translation and insights powered by best-in-class ASR, LLMs, and GenAI models.

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