The common concern and main limitation among the GPT model is Hallucination and Black-box nature, which are a direct result of large language models (LLMs) like ChatGPT and Bard

Generative AI offers exciting possibilities for enterprises, but careful consideration of their drawbacks is necessary to ensure responsible and successful implementation in organisations

To understand how this happens, it’s important to know how LLMs work. The LLM firstly analyze the input (prompt) you provide, It breaks down the text into individual words and analyzes their grammatical structure and relationships, it creates a context for the prompt, considering past interactions (if applicable) and its overall knowledge

The LLM then dives into its massive memory, It searches for patterns within this data that match the prompt, looking for statistically likely sequences of words and phrases.

Based on the identified patterns, the LLM starts predicting the most likely word that would come next in the sequence. The response generation process is dynamic and adapts to the context, considering previous prompts and responses

As LLMs are trained on massive, millions or even billions of data (These data can be text, images, audio, video, or other types of data.) that include natural language conversations, news articles, and other forms of human communication. This exposure allows them to learn the nuances of human language, to generate responses that sound natural and engaging.

In this article you'll get to know about AI hallucinations, the reasons behind their occurrence, other drawbacks associated with Generative AI and LLMs and what can be the potential solution for these challenges.

Let’s Dive in !
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What is Hallucination & Why does Generative AI Hallucinate?
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Generative AI hallucinations refer to a phenomenon where a large language model (LLM), perceives patterns or objects that are nonexistent or imperceptible to human observers. This results in outputs that are nonsensical or altogether inaccurate, often due to limitations in training data, giving wromg prompts, model architecture, or understanding of the real world. These can range from factually inaccurate statements to completely fabricated content, manifest in various ways, like-  Inventing facts, statistics, quotes that don't exist or producing contradictory outputs.

Does Generative AI answers can be nothing but just Hallucinations, keeping the users in mystery from where the answers came from?

Studies suggest a range of rates for factually incorrect outputs in LLMs, depending on the specific model, task, and evaluation criteria. Some studies report rates between 5-10%, while others mention 20-30%.

Rate of hallucination: A 2022 study by Google AI found that large language models (LLMs) hallucinate (generate incorrect or misleading text) about 10% of the time. The study also found that the rate of hallucination increased as the length of the generated text increased.

• Limited training data: Generative models learn from the data they're trained on. If the data is limited in scope, diversity, or quality, the model may not have enough information to accurately learn the real world and its complexities.
• Biases in the data: If the training data contains biases, the model will likely learn and perpetuate those biases in its outputs. This can result in hallucinations that are discriminatory, offensive, or simply inaccurate reflections of reality.
• Model limitations: Sometimes, models trained on large datasets can become overly focused on memorizing specific patterns and fail to generalize well to new situations. This can lead to hallucinations when presented with prompts or situations that deviate from the memorized patterns.
• Computational limitations: Training and running large Generative AI models require significant computational resources. Limitations in these resources can hinder the model's ability to process information accurately and generate reliable outputs.
• Ambiguous or unclear prompts: If the user provides an unclear or incomplete input, the model may misinterpret it and generate something irrelevant or nonsensical.
• Complexity of the task: More complex tasks are naturally more prone to hallucinations as they require the model to understand and manipulate larger amounts of information and relationships.
• Lack of interpretability: It's often difficult to understand the reasoning behind a model's outputs, making it challenging to identify and correct hallucinated response.

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Organisations cannot afford to provide any single false information to their customers !

Why hallucination is a problem?

What if your company heavily relied on an AI solution for automation, question answering, creative content generation, where fact-checking is not possible? In such scenarios, the occurrence of AI hallucinations could potentially lead to significant risks for your business.

• Flawed Decision: If generative AI models produce inaccurate or misleading information (hallucinate), it can lead to flawed decisions at various levels, impacting finances, marketing/ sales strategies, legal, data science, and more.
• Operational inefficiencies: Misinformation generated by the models could lead to inefficiencies & delays disrupting workflows  and reduce overall productivity.
• Misinformation and harm: Hallucination can spread false information, especially in sensitive areas like finance or healthcare.
• Negative perceptions: Organizations relying on biased or factually incorrect outputs for customer support or sales can lead to irrelevant suggestions and negative customer perceptions.
• Customer service: Chatbots using generative models might provide inaccurate or unhelpful responses, escalating unnecessary tickets, leading to customer dissatisfaction.

Other Drawbacks of Generative AI technology

Technical limitations:

• Interpretability ("GPT Black box" nature): Understanding how generative models arrive at their answers can be challenging. This lack of transparency makes it difficult to assess their reliability and trustworthiness for critical decisions.
  Whats would be the point of saying Gen AI can provide answers to every question but the user would not know from where the answer came from.
  The intricate architecture and vast number of parameters in LLMs make it difficult to trace their decision-making process.
• Accuracy and Reliability: Generative AI models are trained on massive datasets, and any biases or errors in that data can be reflected in their outputs. This can lead to inaccurate or misleading results, especially for complex tasks.
• Limited Creativity: While effective at mimicking existing styles and patterns, generative models often struggle with genuinely original ideas or solutions. They might get stuck in repetitive outputs or fail to grasp truly novel concepts.
• Lack of Contextual Understanding: While adept at generating content, generative models might struggle with nuanced contextual understanding. This can lead to outputs that miss the mark.
• Explainability and Debugging: Debugging errors and understanding model outputs can be challenging, requiring specialized skills and effort.

Operational challenges:

• Data Privacy and Security: Generative AI leverages large amounts of data, raising concerns about privacy and security. Enterprises need robust data governance practices to mitigate risks of misuse or leaks.
• Ethical Considerations: Deepfakes and other potential misuse of generative technologies can damage brand reputation and fuel misinformation. Companies must establish ethical guidelines and responsible deployment strategies.

Training & Maintainence

• High Implementation Costs: Training and maintaining these powerful models requires significant computational resources and skilled personnel, making them expensive for some businesses.
• Technical expertise: Implementing and maintaining generative AI solutions often demands specialized technical skills, which may not be readily available within all organizations.
• Integration with existing systems: Integrating generative AI with existing IT infrastructure can be complex and require careful planning and development.

Strategies Fluid AI has implementd to Mitigate Risk of Hallucination & Black-Box nature of Generative AI technology

Making the Generative AI technology Enterprise-ready !

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• Customizing LLM Models on Organizational Data: LLM models rely on data sets that become old gradually. So your organisational data, products/services information, website data is not present in the model. In order to improve the accuracy of the model, its important to provide relevant context information, ensuring training data is diverse, unbiased, and representative of the real world. However, it is complex to retrain the entire model with constant updates in the information, its nearly impossible
  
  Fluid AI has made things easy for enterprises, by allowing enterprises to build AI Knowledge Base to customize LLM models with their organizational data. We support multi-modal capabilities, i.e you can upload all form of data- txt, pdf, docs, videos, audios, images, url, etc. This helps prevent hallucination and enables the model to accurately handle organization-specific queries, whether customer-facing or internal.

• Increase reliabilty with Anti Hallucination Shield: Fluid AI has implemented an Anti-Hallucination shield within the GPT Copilot to prevent inaccurate responses. This shield ensures & indicates that the output is from the organization's built Knowledge Base.  With this leel of  transparency, organizations can determine if the output is from their own AI knowledge base or from pre-trained knowledge of the model, increasing reliability on the output and the technology.
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• Explainable AI (XAI) that explains reasoning and output of the models: The black-box nature of AI technology often makes it challenging for organizations to trust the accuracy of the generated responses. What would be the point if I say that Generative AI can help to quickly provide answers to any queries but you would not know if the response is factual & accurate & difficult to verify or cross-check the information provided. This can lead to wasted time, effort, and delayed decision-making.
  
  However, with Fluid AI's Explainable AI (XAI), transparency is ensured by revealing the sources that the GPT Copilot utilized to generate each answer. By providing snippets of the original sources, the black-box nature of the technology is eliminated, allowing organizations to have greater confidence in the output.

• Fact-checking and validation: Fluid AI has Implemented the mechanisms to verify the accuracy of generated information. The Fluid Copilot have capability of AI smart scroll, that is along with the snippets of sources, the copilot will provide referenceable link to the user can easily track & cross check the output without leaving the platform or doing manual searches through thousands of doc.
  
  Additionally the copilot also indicate the Confidence score with every document indicating the reliability of the referred source by the GPT copilot for any particular output indicating the top reliability.
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• Not all Gen AI chatbot is powered by RAG Capabilities: Retrieval-Augmented Generation (RAG) is a powerful technique that's revolutionize Generative AI chatbots by combining the strengths of retrieval and generation models. Fluid AI GPT Copilot consist the capability of RAG to actively search and retrieve relevant information from external sources like knowledge bases, documents, your organisations CMS, and even the internet unlike traditional Generative AI that rely solely on their pre-trained knowledge and the input they receive in the moment
  
  RAG represents a powerful tool for combating generative AI hallucinations by providing factual grounding, improving context awareness, and increasing transparency
  
  To know more about what RAG powered chatbots can do & how it empower traditional Gen AI tech, visit our blog- RAG: Technology that combines power of LLM with real-time knowledge sources
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• Model Selection and Evaluation: Choose models with good performance on relevant tasks and employ rigorous evaluation methods to detect and address hallucinations.
  Fluid AI offers the flexibility for users to choose and work with any LLM model available in the market. Additionally, users can easily switch to the latest model in the future, ensuring they have access to the most latest & advanced technology without any setbacks.
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• Consult with an expert in the field of generative AI: Experts have a comprehensive understanding of the capabilities, limitations, and potential pitfalls of generative AI models. They can help you navigate the complexities of this rapidly evolving field and identify the best solutions for your specific needs.  Having the support and guidance of an expert can provide peace of mind and reduce the uncertainty associated with adopting a new technology.
  
  At Fluid AI we are professionals, worked on various generative AI projects & have done successful implementations across industries, helping organizations kickstart their AI journey. If you’re seeking a solution for your organization, look no further. We’re committed to making your organization future-ready, just like we’ve done for many others.
  Take the first step towards this exciting journey by booking a free demo call with us today. Let’s explore the possibilities together and unlock the full potential of AI for your organization. Remember, the future belongs to those who prepare for it today.
  
  We work with our customers to develop a comprehensive strategy for implementing and utilizing generative AI responsibly and effectively, addressing potential issues like bias, explainability, and security mitigating AI risks. Further it also saves time, high maintainence cost, efforts and complex Integration of AI with existing systems. Additionally, we help organisations eliminating the need for hiring additional teams or struggling to keep up with the latest technological advancements, provide comprehensive support throughout your AI journey.