An LLM hallucination refers to an output generated by a large language model that’s inconsistent with real-world facts or user inputs. RAG helps avoid them.
What is an LLM?
A Large Language Model (LLM) is a specialized type of Artificial Intelligence (AI) that’s been trained on vast amounts of textual data. An LLM can perform a variety of Natural Language Processing (NLP) tasks like answering basic questions.
LLMs are trained on diverse datasets containing text from publicly available sources – like books, articles, and websites – enabling them to “understand” and mimic intricate language patterns (grammar and semantics and more), context, and nuances. Thus, an LLM can generate coherent, contextually relevant responses. Some common applications of LLMs include writing (articles, poems, or stories), translating, summarizing, responding to queries, and even generating code.
Notable LLMs include OpenAI ChatGPT, Meta Llama, Google Gemini, and IBM Granite. Although LLMs continue to evolve, they’re already reshaping how we access information and interact with machines without a human in the loop.
What is an LLM Hallucination?
A large language model hallucination is a form of AI hallucination in which the LLM generates content that’s inconsistent with real-world facts or user inputs.
Why do hallucinations happen? LLMs exploit patterns in massive datasets to predict the most likely continuations of a sequence allowing them to generate grammatically correct and “seemingly” coherent text. This fixation on statistical likelihood often causes the LLM to generate text that’s factually incorrect or internally inconsistent – called hallucinations. Lacking the ability to verify information or grasp real-world context, LLMs can spin a yarn that sounds totally plausible, based on its training data, even if it’s entirely fabricated.
The possible dangers of LLM hallucinations are considerable. Misinformation can easily spread when an LLM confidently delivers an invented response. And hallucinations can undermine trust in LLMs as reliable sources. Because of this, AI scientists are attempting to limit LLM hallucinations with techniques like retrieval-augmented generation vs fine-tuning and fact-checking.
What Causes an LLM Hallucination?
Most LLM hallucinations are caused by faults in AI data, model training, or in the response process, notably:
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Data deficiencies
Training data may contain biases, factual errors, or incomplete information. The LLM, lacking real-world understanding, inherits these flaws and perpetuates them in its outputs.
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Statistical blind spots
LLMs excel at predicting the next word in a sequence based on probabilities – but they can't tell true from false. A perfectly plausible claim might align with the model's learned patterns – even if it’s entirely fabricated.
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Context constraints
LLMs often provide responses based on limited context. Lacking broader information, they can misinterpret a prompt or generate responses that are internally consistent but contextually irrelevant.
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Overfitting
LLMs are often too focused on memorizing training data – like students cramming for a test. This generative AI phenomenon is called "overfitting" because an LLM may overly rely on memorized patterns, leading it to generate irrelevant or nonsensical outputs when presented with fresh information.
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Limited reasoning
LLMs can't grasp cause-and-effect relationships or understand the logical flow of information. These limitations can lead to hallucinations where the generated text might be grammatically correct but ridiculous.
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Ambiguous prompts
An LLM relies on clear prompts and context. If a user’s prompt is ambiguous or misleading, the model might fill in the gaps based on its understanding – which can lead to a hallucinatory response.
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Algorithmic bias
Biases present in training data can be reflected in LLM outputs, resulting in hallucinatory outputs that stereotype or discriminate.
How to Reduce LLM Hallucination Issues
LLM grounding reduces hallucinations via:
- Improved training data
For LLMs, the knowledge sources matters. Using high-quality data that has been fact-checked and includes diverse perspectives helps LLMs learn from reliable sources. Better training reduces inherited biases or factual errors that lead to hallucinations. - Fact-checking mechanisms
Various mechanisms for integrating fact-checking during LLM response generation allows models to verify information against trusted sources in real time, thus reducing the likelihood of hallucinations. - Context expansion
When provided with more information about the context of the topic or prompt, LLMs can generate more focused and relevant responses. This context lowers the risk of false or silly responses. - Uncertainty quantification
LLMs can be trained to judge themselves by estimating the veracity of their responses. With this information, users can identify potentially unreliable responses. - Regularization techniques
LLM techniques, like dropout or early stopping during training, can mitigate the effects of overfitting. Such practices help LLMs generalize better and avoid relying solely on memorized patterns from the training data.
Retrieval-Augmented Generation Ends Hallucinations
Retrieval-augmented generation (RAG) is a proven way to prevent LLM hallucinations. It can access and supplement unstructured and structured data from any of your company’s data sources using an innovative approach called GenAI Data Fusion.
Based on data-as-a-product methodology, GenAI Data Fusion unifies data for individual business entities (like customers, orders, or devices) from enterprise systems and knowledge bases. This mean that you can turn the data from your customer 360 platform, for example, into contextual prompts. The prompts are then fed into the LLM along with the user’s original query, enabling it to generate a more personalized and reliable response.
With GenAI Data Fusion, RAG accesses data products via API, messaging, CDC, or streaming – in any combination – to aggregate and unify data from virtually any number of source systems. A data product approach can be applied to various RAG conversational AI use cases to:
- Resolve issues faster.
- Create AI-personalized marketing campaigns.
- Personalize cross-/up-sell recommendations.
- Detect fraud via suspicious activity seen in user accounts.
Reduce LLM hallucinations with the market-leading RAG tool – GenAI Data Fusion by K2view.
