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.
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.
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.
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.
While hallucinations remain a challenge, several strategies can help reduce their frequency and impact.
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.
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.
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.
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.
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.
Speech-To-Text
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.
Case Studies
In recent years, fuelled by advancements in LLMs, the numbers of AI note-takers has skyrocketed. These apps are increasingly tailored to meet the unique needs of specific user groups, such as doctors, sales teams and project managers.
Speech-To-Text
It happens all the time. A user submits a query to a large language model (LLM) and swiftly gets a response that is clear, comprehensive, and obviously incorrect.
