Understanding Hallucinations in Large Language Models (LLMs)
Dr. Hassan Sherwani
Data Analytics Practice Head
April 12, 2024
Build AI Models Free of Hallucinations with Royal Cyber
In artificial intelligence (AI), large language models (LLMs) have made significant strides, revolutionizing various industries with their natural language understanding capabilities. However, alongside their prowess in generating coherent responses, LLMs sometimes exhibit a phenomenon known as hallucinations.
Read our case study to learn how we helped our client analyze customer reviews with LLMs.
This blog aims to be a guide to hallucinations in LLMs by delving into the intricacies of LLM hallucinations and exploring their causes, potential utility, and mitigation strategies.
What Does it Mean for an LLM to Hallucinate?
Hallucination in LLMs refers to instances where these models generate text that deviates from factual accuracy or coherence. Despite extensive exposure to diverse datasets during training, LLMs may produce outputs that need more factual foundation or relevance to the provided prompts. Users often unknowingly rely on these responses, leading to detrimental outcomes in fact-driven domains like law, healthcare, and finance.
There are several examples of LLM hallucinations in recent times:
- In 2023, a lawyer was fired from his law firm when it was found that his motion, which ChatGPT drafted, had created fake cases in the document.
- An amusing example is ChatGPT, who, when asked how Mahatma Gandhi used Google LLC Suite to organize against the British, earnestly answered that Gandhi used Gmail to send emails and collaborated on projects with Google Docs.
- Microsoft Bing Chat was 2023 involved in a libel lawsuit due to their chatbot generating statements that conflated an author on aerospace with a convicted terrorist.
Hallucinations in LLMs pose a significant threat, especially considering findings such as the study by Capgemini Research Institute, which found that 73% of consumers trust content created by generative AI tools. With this high rate of adoption and trust of chatbots and tools like ChatGPT, Microsoft Copilot, and more, which have varying rates of hallucinations (ChatGPT was found to hallucinate information 3% of the time, while the error rate for Google PaLM was 27%), companies must ensure that they control the different types of hallucinations and prevent such errors from potentially impeding their operations.
What Causes LLMs to Hallucinate?
There are several causes of LLM hallucinations, including:
Training Data
- LLMs undergo training on vast and varied datasets, making verifying the accuracy and fairness of the information they ingest challenging.
- Factual inaccuracies in the training data can lead to the model internalizing and regurgitating incorrect information during text generation.
Lack of Objective Alignment
- When repurposed for tasks beyond their original scope, LLMs may exhibit random hallucinations due to their inability to discern facts from untruths.
- Domain-specific tasks such as medicine or law require additional guidance for accurate inference, as LLMs are primarily trained for general natural language processing.
Prompt Engineering
- The quality and clarity of the prompt provided to LLMs significantly influence their output.
- Ambiguous or insufficient prompts can result in incorrect or irrelevant responses, contributing to hallucinations.
Can Hallucinations be Useful?
While hallucinations in LLMs pose risks in fact-driven contexts, they can also be leveraged for creativity and diversity in specific applications. For instance:
- Creative storytelling: LLMs capable of hallucinating beyond training data can craft original narratives and storylines, fostering creativity.
- Idea generation: Hallucinations facilitate the exploration of diverse ideas and perspectives, aiding brainstorming sessions and innovation efforts.
How Do You Solve LLM Hallucinations?
Mitigating hallucinations in LLMs requires a multifaceted approach, incorporating techniques such as:
Context Injection
Providing sufficient context within the prompt helps LLMs (Language Model Models) generate more accurate and relevant responses. Clear and detailed prompts are crucial as they reduce ambiguity and minimize the risk of generating inaccurate or irrelevant responses. So, including all the necessary details while framing the prompts is essential to ensure the LLMs can understand the context and provide the most appropriate response. This will help you save time and avoid confusion.
One-shot and Few-shot Prompting
One effective way to improve the performance of language learning models (LLMs) is to use one-shot and few-shot prompting techniques. These techniques involve providing the model with examples of desired responses, either one at a time (one shot) or a few at a time (few shots), to train the model to generate more accurate and relevant responses to prompts.
In particular, few-shot prompting has shown to be a more robust approach, as it allows LLMs to be furnished with additional context for generating responses. This additional context can include information such as the speaker’s tone, the topic of conversation, and any relevant background knowledge that might be needed for the response. By providing this context, the LLM is better able to generate responses that are not only accurate but also more natural and human-like.
Another effective technique for improving LLM performance is restricting response length. By limiting response length, LLMs must focus on generating more precise and concise responses. This can help reduce the model’s risk of generating irrelevant or tangential responses, which can be problematic with some language models.
Overall, using one-shot and few-shot prompting techniques, along with response length restriction, can significantly enhance the performance of LLMs, making their responses more accurate, relevant, and natural.
Retrieval-Augmented Generation (RAG)
RAG, or Retrieval-Augmented Generation, is a technique that enhances the performance of LLMs. It does so by integrating domain-specific knowledge into the prompt, thereby improving the accuracy and relevance of the generated outputs.
RAG mitigates hallucinations by supplementing the prompts with information from relevant databases or knowledge bases.
For instance, if an LLM generates text about a specific medical condition, RAG can supplement the prompt with information from relevant medical databases to ensure that the output is accurate and relevant. This approach allows the LLM to generate fluent, factually correct outputs relevant to the context.
Overall, RAG is a powerful technique that can significantly enhance the performance of LLMs, making them more accurate, relevant, and reliable in various domains.
Domain-specific Fine-tuning
Fine-tuning language models (LLMs) refers to updating the pre-trained models with additional data from a specific domain. This process aims to align the model’s knowledge with the target domain, making it more accurate and reducing the likelihood of generating irrelevant or incorrect responses. This approach enables LLMs to adapt their responses to the nuances and requirements of specific industries or fields, such as legal, medical, or financial domains.
The model can better understand the vocabulary, syntax, and context used in that domain by fine-tuning LLMs on domain-specific datasets. This, in turn, leads to more accurate and contextually relevant responses. Furthermore, fine-tuning allows the model to learn from the specific patterns and structures of the target domain, resulting in improved performance in tasks such as text classification, sentiment analysis, and question-answering
Fine-tuning LLMs on domain-specific datasets is crucial to achieving better performance and accuracy in natural language processing tasks, especially in specialized domains where accuracy and relevancy are critical.
Want to learn how our experts can help you build a domain-specific AI model or fine-tune them? Download our infographic on our Generative AI services.
How Royal Cyber Can Help Control LLM Hallucinations
Enterprises seeking to deploy LLMs without the risk of hallucinations can benefit from Royal Cyber’s expertise in fine-tuning and data labeling. Our team of experts specializes in refining LLMs with accurately labeled data tailored to specific domains, ensuring optimal performance and reliability. By leveraging our services, enterprises can mitigate the challenges posed by LLM hallucinations and harness the full potential of AI in their operations.
In conclusion, while hallucinations in LLMs present challenges in maintaining factual accuracy and coherence, they also offer opportunities for creativity and diversity in AI applications. By understanding the causes of hallucinations and implementing effective mitigation strategies, enterprises can harness the power of LLMs while minimizing the risks associated with erroneous outputs. With Royal Cyber’s support in fine-tuning and data labeling, enterprises can build robust LLMs tailored to their specific needs, paving the way for enhanced efficiency and innovation in AI-driven solutions. For more information, visit our website, www.royalcyber.com, or contact us at info@royalcyber.com.
Author
Priya George
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