LLM Hallucination—Types, Causes, and Solution

The development of large language models (LLMs) and generative AI solutions, notably demonstrated by ChatGPT’s impressive abilities, is changing how we use AI systems. IBM research found that nearly 50% of CEOs report adopting Generative AI in their companies.

However, this progress is delayed because of a phenomenon known as LLM hallucination. The term describes when LLMs produce text that is incorrect, makes no sense, or is unrelated to reality. A Telus survey highlighted this concern, revealing that 61% of people are worried about the growing problem of false information on the internet. There is a critical need to tackle the issue of hallucination in LLMs and ensure responsible application of Generative AI and LLM technology.

This article examines the issue of LLM hallucination, its effects on AI performance, and the types and causes of such errors. It also outlines strategies to reduce hallucination, aiming to improve LLM reliability in various applications.

Summary of key LLM hallucination concepts

What is LLM hallucination?

Hallucination in LLMs refers to an error where these AI systems generate output that is inaccurate, irrelevant, or simply does not make factual sense. For instance, an LLM might create fictional events, misstate facts, or produce contradictory statements within the same text.

The term “hallucination” is metaphorically used to describe how these models, much like a person experiencing a hallucination, “see” or “create” something imaginary. These errors do not result from a programmed process but arise from the limitations and complexities inherent in LLM training and data interpretation. When LLMs generate hallucinated information, it directly undermines the trust users place in these AI systems. Hallucinated outputs can also cause significant real-world consequences.

For example, ChatGPT inaccurately summarized the Second Amendment Foundation v. Ferguson case, wrongly accusing Georgia radio host Mark Walters of defrauding and embezzling funds from the foundation. Walters has filed a lawsuit against OpenAI for this error.

LLM hallucinations also add to the challenges of bringing LLMs into production. Developers must invest in creating more sophisticated training datasets, improving model architectures, and implementing safeguards. Expanding their use necessitates continuous observation and frequent updates to omit the occurrence of hallucinations.

Types of LLM hallucination

Hallucinations in LLMs manifest in various forms. Understanding these types is important for identifying and mitigating them.

Factual inaccuracies

This type of hallucination occurs when LLMs generate incorrect or misleading information. For example, an LLM might present inaccurate historical information,  scientific facts, or biographical details. It might be claimed that “Thomas Edison invented the internet” when, in fact, the internet was developed in the late 20th century, long after Edison’s time. 

Factual inaccuracies significantly undermine the trustworthiness of LLM outputs in applications related to educational content, journalistic articles, or professional advice. 

Nonsensical responses

Sometimes, LLMs generate completely irrelevant responses to the input prompt. Nonsensical responses highlight the limitations of LLMs in understanding context or maintaining a logical thread in conversation. They pose challenges in user interaction scenarios where clarity and relevance are key.

Contradictions

LLMs can produce text that contradicts itself within the same output or across different interaction instances. For example, an LLM might state a fact in one sentence and then provide a conflicting statement later. Such contradictions arise from 

• Input-conflicting hallucinations, where the model’s output does not align with the given input, or
• Context-conflicting hallucinations, where the model contradicts information it previously generated. 

Studies on self-contradictory outputs in LLMs show a notable occurrence of these issues. For instance, ChatGPT has been found to have a contradiction rate of 14.3%.

Examples

A good systematization approach to hallucination types through examples is done by Zhang et al. We present a table from their research below.

Causes for LLM hallucination

You can attribute hallucinations in LLMs to a range of factors.

Training data issues

If the data used to train LLMs lacks quality or diversity, the model does not learn to accurately understand the complexities of human language. Issues that can confuse the model include

• Inadequate representation of topics 
• Presence of biases or outright misinformation in training data
• Noise in the training data, such as errors, inconsistencies, or irrelevant information, leads to the generation of factually incorrect responses.

Model limitations

LLMs may struggle to generalize from their training data to new contexts due to overfitting. Overfitting occurs when a model performs well on its training data but fails to produce accurate outputs in real-world applications.

LLMs may also have limitations in fully understanding the context or intent behind user prompts. Their ability to perform logical inference based on the provided input is limited. LLMs cannot yet respond by asking the user more questions for clarification. Instead, they just generate outputs based on flawed reasoning or incomplete knowledge.

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Limited context window

LLMs are constrained by a maximum context window, meaning they can only consider a certain number of tokens (words) simultaneously. This limitation leads to misunderstandings or omissions of crucial information, especially in longer conversations or documents. The model loses context over extended interactions. When the input exceeds limits, the model generates responses based on a partial understanding of the prompt, potentially leading to contradictions or irrelevant answers.

Nuanced language understanding

LLMs struggle with interpreting the subtleties of human language, including irony, sarcasm, and cultural references. LLMs may generate outdated or irrelevant information in situations where nuance is key to understanding the intent behind a prompt.

It should be highlighted that LLM limitations place a significant burden on users to craft exceedingly clear and detailed prompts. Users must often adapt their queries to fit the model’s capabilities to avoid hallucinations.

Best practices to reduce LLM hallucination

Mitigating hallucination in LLMs (Source)

Here are four key ways to reduce hallucinations in your applications.

Pre-processing and input control

Set a limit on the input/output length for both the end user and the LLM. This ensures the text stays relevant and makes interactions more meaningful. You can encourage the model to generate concise responses by

• Focusing on conciseness in fine-tuning data
• Employing few-shot prompting in prompt engineering that emphasizes brevity.

This strategy reduces the likelihood of hallucinations, as fewer tokens mean fewer opportunities for the model to drift into incorrect directions.

Similarly, you can give users set prompts or style options instead of a blank text box to guide what the model produces. This approach limits the range of possible answers and lowers the chance of getting hallucinated responses by giving clear directions.

Adjusting model configuration

You can regulate model parameters as follows. 

• Change the temperature to affect how predictable or varied the responses are. A lower temperature leads to more predictable text, while a higher temperature allows for more creativity and randomness. 
• Increase the frequency penalty so the model is less likely to repeat the same words.
• Boost the presence penalty so the model includes new words in the output and improves text diversity.
• Adjust the top-p parameter to only allow words with a specific combined probability for the right mix of diversity and relevance in the responses. 

You may also consider adding a moderation layer to the model to remove any inappropriate, unsafe, or irrelevant content. This ensures the responses adhere to your security standards and guidelines for high-quality and safe output.

Monitoring and improvement

An active learning approach helps refine the model based on real interactions. For example, you can 

• Implement a system that collects user feedback and then adjusts to meet their needs.
• Perform thorough testing to find and address any bugs that could lead to incorrect outputs. 
• Regularly include human verification of model responses and track the model’s performance over time.
• Perform domain-specific improvements by introducing knowledge specific to your particular area of application. 

The next-generation Nexla data platform can help you do all of the above with minimum effort. For example, you can enhance LLMs with custom data using Nexla’s custom transformation feature to convert free-text data into vector embeddings without coding. You can also use it to automate the collection and integration of user feedback into the training pipeline.

Enhance context in production

You can add more context at run time. For example, you can give the model access to up-to-date external data sources during its prediction phase. This approach allows the model to provide more precise and better-matched answers. Nexla can facilitate real-time access to external databases and knowledge bases, providing LLMs with up-to-date information to reduce the chances of LLM hallucination. 

You can also take user prompts and enhance them further with clear instructions, contextual hints, or specific framing methods to direct the LLM’s output generation more effectively. Detailed prompts minimize confusion and enable the model to produce accurate and logically consistent answers.

Last thoughts on LLM hallucination

LLM hallucination highlights developer challenges in developing AI systems that understand context, maintain coherence, and reliably produce accurate information. They must actively reduce LLM hallucinations in enterprise use cases to avoid real-world consequences. Teams have to check for hallucination when prototyping the initial build and continuously monitor and iterate to ensure LLM hallucination does not make its way into users’ hands.

The key to effectively minimizing hallucinations lies in a multifaceted approach that covers everything from model pre-processing to ongoing maintenance and updates.
Advanced tools like Nexla are helping teams reduce LLM hallucination at every stage, from prototyping to post-production and user feedback.

Fine-tuning models with fresh and reliable data is crucial to finding patterns grounded in reality. Strategies like LLM parameter adjustment, contextual prompt engineering, and advanced tools like Nexla are a must to stay ahead in the AI game.