There’s a fascinating similarity between everyday human interactions around the coffee machine in your office and interactions we can have with Large Language Models (LLMs).
LLMs are massive neural networks, meticulously trained on vast and diverse datasets so they can comprehend and generate human-like text in response to user questions (called “prompts”). This training approach is not dissimilar to the way we learn—through reading, exchanging knowledge with others, and our own lived experiences.
It’s not uncommon for us to misremember or exaggerate the stories we tell, and, similarly, LLMs can occasionally infuse their responses with elements of creative interpretation. We call these “hallucinations,” where hard data combines with imagination to give an answer that’s just not quite right.
While we can’t ensure that every story retold is 100% factual, we can solve for LLM hallucinations.
What are LLM hallucinations?
LLMs excel in a wide spectrum of language-related tasks, from translation to summarization, text generation, and more. A hallucination is when an LLM confidently responds to a user prompt with information that is incorrect or nonsensical.
Why do hallucinations occur?
- Pattern extrapolation: LLMs are essentially pattern recognition machines driven by their training data. They can identify patterns, linguistic structures, and associations, but they lack an intrinsic understanding of context, causality, or common sense. Without common sense or a human’s innate understanding of true and false (which is, of course, inherently fallible), they can’t identify when an answer drawn from a pattern found in their dataset doesn’t make sense.
- Ambiguous or open-ended prompts: Hallucinations often originate from prompts or questions that lack specificity, contain ambiguity, or invite speculation. Vague prompts provide room for interpretation, sparking responses based on what they have learned, but not necessarily what you intended. Language itself carries inherent ambiguity, with different possible interpretations and contextual dependencies. Misunderstanding the context or ambiguous language can cause the LLM to generate creative responses in unexpected directions.
- Data diversity and limitations: LLMs are trained on extensive and diverse datasets that expose them to a wide range of ideas and writing styles. This diversity allows them to draw connections between concepts that might not naturally occur to us. This can be a major benefit in many contexts and contributes to their ability to generate creative responses, but it can sometimes result in strange answers. At the same time, LLMs are trained on defined datasets, so if you ask it about a topic it doesn’t have information on without sharing the context, you may get an incorrect answer.
How to mitigate and minimize your risk of LLM hallucinations
For financial services firms, trust in and reliability of your systems, software, and data quality are paramount. The thought of AI systems introducing imaginative, unexpected outputs—hallucinations—naturally raises concerns about their usability in an enterprise context, but it doesn’t need to deter you from embracing their immense potential for your business.
The right approach is to mitigate against these risks and adopt strategies to minimize the risks of hallucinations, allowing your business to confidently leverage LLMs in your workflows.
Implementing the following strategies can minimize the risk of hallucinations and bolster trust in LLMs:
- Boundary controls: Apply guardrails to LLM-powered applications by sending relevant context to the LLM that supplements the user’s initial prompt. This creates boundaries around the sources the LLM derives its response from and reduces the risk of the model generating a response that is not based on information directly from your document.
We built Alpha to derive all answers from the corresponding document or dataset. These guardrails ensure every response comes directly from our customers’ documents and can be easily verified.
- Temperature control: LLMs include a parameter known as temperature, which allows you to regulate the degree of randomness in the model’s responses. Lower temperature values result in more focused responses, whereas higher values introduce more randomness and creativity. Leverage temperature control to strike a balance between accuracy and precision and creativity and novelty, depending on the task at hand.
Our solutions have undergone immense testing to optimize temperature and strike the right balance for our customer needs. With our focus on enterprise customers and financial services use cases, as opposed to LLM use cases in creative fields, we decided to set our products to a lower temperature setting.
- Traceability & human-in-the-loop review: Implement a post-processing validation step where your subject matter experts review and verify the outputs generated by LLMs. This human oversight ensures the quality and accuracy of the generated content.
When building Alpha and thinking about our customers’ needs, we built in the ability to trace all responses to the source location in the document, enabling easy data validation.
- Clear and specific prompts: Craft prompts that are clear, specific and limit room for interpretation. Well-defined instructions with relevant context can guide LLMs towards more accurate responses.
As LLMs continue to learn and adapt, they will be further woven into all of the products that we use. The question for businesses won’t be whether or not to implement LLMs, but instead, how do we build sufficient guardrails to ensure business continuity and reliability? Products powered by LLMs will continue to transform what is possible with data, and not adopting this evolving new technology will put companies at a competitive disadvantage. With the right strategies in place to mitigate the risk of LLM hallucinations, you can confidently be at the forefront of AI adoption.