What happens when your AI assistant confidently gives you detailed, but completely made-up information?
This goes beyond any hypothetical situation. The risk is real, especially for CXOs and data leaders, when introducing and integrating gen AI into business workflows.
Generative AI models producing plausible but false outputs called AI hallucinations are silent saboteurs affecting trust and reliability, mounting hurdles for business leaders. They also call into question the data quality in generative AI, challenging the accuracy and credibility of the outcomes.
So, how can organizations ensure data quality and mitigate the difficulties of AI hallucinations? What’s the role of governance in enterprise AI adoption? Let’s explore all of this, and more, in this blog.
Data Quality In Generative AI Models: Why Does It Matter?
If you want reliable results from gen AI, you need a strong foundation - AI data quality. In generative AI, this means ensuring accuracy, completeness, consistency, and relevance of the data being fed for training. This is a non-negotiable because poor quality data doesn’t just affect the outputs but also fuels AI hallucinations
Imagine what this can lead to in high-stakes domains like healthcare. Generative AI models trained on incomplete, biased, and inaccurate datasets can suggest wrong diagnoses, which is highly dangerous. That’s why the vigilance around AI accuracy is growing quickly and consciously. A 2024 McKinsey report reveals that 27% of organizations are already monitoring and reviewing AI-generated content before use. Source
Data quality management is a business imperative for enterprise data leaders. Mismanagement can affect everything - from operational decisions to regulatory compliance.
Addressing AI Hallucinations: A Data Quality Concern In Generative AI Models
What we call a hallucination in generative AI is nothing but the system producing outputs that seem possible but are fabricated entirely or factually incorrect. Data quality in generative AI is the biggest cause of these, and the issues arising can often be traced to:
- Insufficient and/or biased training data: When incomplete or unrepresentative data is fed into models, they are more likely to invent their own facts.
- Chaotic, inconsistent datasets: When data labelling is erratic or there are errors in the data, it can seed inaccuracies. This will multiply with the model’s use.
- Domain-specific data missing: If there is a lack of specific and relevant details pertaining to a special context, models will perform poorly.
A fitting example is that of OpenAI’s O3 model. A recent report on this powerful system showed that it hallucinated 33% of the time at the benchmark PersonQA test, where it was supposed to answer questions about public figures. Their O4-mini model hallucinated at an even higher rate (48%), highlighting the need to ensure data quality to reduce hallucination frequency in large language models and other AI applications alike. Source
How To Prevent Gen AI Hallucination?
You can tame hallucinations in AI using two approaches - data-centric and model-centric.
Data-centric approach:
This is centered around the main player, the fuel for generative AI hallucinations - data. By prioritizing the curation of high-quality, diverse, and representative datasets, the data-centric approach aims to combat the bias in generative AI and address data quality issues.
It doesn’t just end there. Consistent, conscious, and continuous monitoring and validation of the training data is key to enabling timely data cleansing. This way, we are able to catch and remove errors, avoid redundancies, and remove outdated entries before it’s too late and the negative effects are multiplied.
Model-centric approach:
Here, we focus on the capacity of the generative model and implementing the use of RAG, Retrieval-Augmented Generation, to ground outputs in updated and verified data repositories. There is also room to calibrate and adjust the parameters of the models to achieve maximum factual accuracy and verifiability.
Regularizing the model quickly penalizes its complex behavior, encouraging it to provide outcomes that are relevant and aligned with the fed data. This automatically diminishes the chances of false content generation.
But standalone, without human oversight and advanced technology and tooling, neither the data-centric nor the model-centric approach can go far.
Human-in-the-loop:
Humans stepping in is critical to maximize the efficiency of gen AI models and minimize the errors and hallucinations. Incorporate human feedback at every critical step of the way, and schedule regular domain expert reviews to catch and correct AI hallucinations in the production workflows, from the get-go.
Technology & tools:
Tech is our best friend. Error detection and correction can be effectively streamlined by leveraging AI-powered data quality management platforms like Azure Databricks. The right tools and technology also empower AI scalability when data flow and workloads are expanding, with no compromises on performance.
Applications Of Hallucinations In Generative AI Models
Hallucinatory outputs are not always detrimental. There are instances where they drive innovation.
Creativity:
There has been a monumental shift from traditional AI systems with predetermined understanding pathways and predictable outputs to the emerging neural network-based AI with complex, opaque learning patterns delivering inexplicable outcomes. While this obscurity, called the blackbox element, is largely condemned and seen as disconcerting, it also resembles the lateral thinking and human creativity capabilities in a sense.
AI-generated creative pieces are usually outcomes of iterative ideation prompts, each iteration revealing a novel and surprising output that may inspire the next move. In this aspect, hallucinatory outputs are definitely a form of intelligence rather than a risk.
In storytelling and gaming, the most refreshing and unpredictable plots could be one of the hallucinated narratives, and in marketing and art, metaphors and visuals generated stretching reality could be the thought starter or driver for the next big idea.
Data augmentation:
When data availability is limited and overfitting is a concern, data augmentation comes into play to improve the robustness and functioning of AI deep learning models. Training AI models by augmenting data sometimes creates hallucinations, which helps reduce their likelihood too, as:
- Datasets expand. By generating diversified datasets, we make the training data comprehensive, better generalize the model, and avoid overfitting.
- Data deficit is addressed. Introducing domain-specific, synthetic but relevant examples and information in cases where the available data is not fully representative of the scenario enables the model to handle ambiguous situations without hallucinating.
- Biases are on check. A constantly hallucinating AI tool or system may be a reflection of prejudices, stereotypes, or underrepresentation in the dataset. This insight, revealing the gaps and issues in quality, can be acted on in better training to enhance a model’s performance.
- Contrastive learning is introduced. With techniques like DPA (Data-augmented Phrase-level Alignment), creating pairs of incorrect and correct responses, models are able to distinguish between the two and catch hallucinated parts in outputs.
While there does exist a good side to hallucinations, we cannot ignore the risks - misinformation and compliance risks, they pose in high-stakes industries:
- Healthcare: Inaccurate, fallacious medical information and misdiagnoses generated by AI can cost a lot. It could even lead to patient harm.
- Finance: Cooked-up risk ratings and generative AI data analytics that are false may lead to poor investment/lending decisions and also affect regulatory reporting.
- Legal: Think about the erroneous advice that could stem from a legal case built on the basis of non-existent and fabricated laws, reference cases, and citations produced by an AI legal assistant. The implications will cost you trust and credibility.
Role of Enterprise Data Governance in Reducing Hallucinations
To mitigate the risk brought about by hallucinations, business organizations must adopt strong data governance and regulatory frameworks. This serves as the backbone of high-integrity AI systems, and ensures that all policies, standards, and processes that safeguard the quality and compliance of data are met.
Governance pillars:
- Data stewardship: Assign ownership and accountability for the data lifecycle
- Metadata management: Track origins of the data, its transformations, and usage forms
- Quality assurance: Enforce data accuracy and consistency standards, and validation checks across the enterprise.
- Audit trails: Maintain logs to trace outputs back to source data. This aids transparency for regulatory compliance.
Benefits:
- Boosts trust in AI-generated decisions
- Reduces bias in generative AI
- Ensures regulatory compliance (e.g., HIPAA, GDPR) in stringent industries
- Supports model retraining with transparent version control
The Future: Can We Eliminate AI Hallucinations Entirely?
While advancements in technology help reduce hallucination in generative AI significantly, eliminating them is not in the scope of what’s achievable today.
Current limitations:
Inherently complex language models
Claude. Gemini. GPT-4. All LLMs generate texts based on massive sets of training data, learning patterns, and statistical likelihood. And, this high reliance on probabilistic reasoning over fact-checking makes hallucinations a structural by-product of the model’s operation.
The same is true even for the most advanced models trained to maintain high factual accuracy. The hallucination frequency such LLMs exhibit varies based on use case and industry domain.
Complicated data ecosystems:
When it comes to enterprise data, it is often siloed, inconsistently labelled, and outdated. This itself is a limiting factor, constraining the model from grounding its outputs in truth and facts. Moreover, over time, the drift, decay, and duplication of data pipelines become more pronounced, further exacerbating the hallucination-associated problems and risks.
Without proper data validation and lineage tracking, model staleness sets in, degrading the performance of the top trained models too.
Advances on the horizon:
While the complete eradication of hallucinations may not be feasible, reducing their frequency, impact, and detectability is well within reach:
Improved data quality tools and governance
Modern data quality platforms are now equipped with AI-powered anomaly detection, semantic validation, and metadata tagging, enabling organizations to identify risky or low-quality data before it enters model training pipelines. These tools integrate with enterprise data governance frameworks, ensuring continuous compliance and auditability.
More robust model architectures
Emerging architectures like Mixture-of-Experts (MoE) and multi-agent models offer dynamic specialization, where sub-models handle different domains and validate each other’s outputs. Additionally, RAG frameworks are gaining traction. They combine generative capabilities with knowledge retrieval systems to reduce reliance on memory and improve factual grounding.
Hybrid human-AI systems
Rather than aiming for full autonomy, enterprises are embracing human-in-the-loop (HITL) models that pair generative AI with expert reviewers. These systems combine AI’s scale with human judgment, enabling real-time correction of hallucinations and faster feedback loops for retraining.
From Hallucinations to High-Integrity Generative AI
Tackling the intertwined challenges of hallucinations and data quality is central to the adoption of generative AI in businesses. When the latter improves, the frequency of occurrence of the former and its associated risks drops, making enterprise-wide AI adoption safer, more scalable, and strategic.
Many companies investing in rigorous AI-powered data quality management and governance are observing a reduction in risks stemming from hallucinations, and thus unlocking the full potential of Gen AI. In order to build such a dependable, trustworthy AI system, partnering with experts who lead at the intersection of data, technology, and industry strategy is key.
Join hands with Tredence to deliver actionable, high-value Gen AI solutions tailored to your enterprise. Bridge the gap between insights and impact!
FAQs
1. What is gen AI hallucination, and how is it different from model error?
Any factually false output that sounds possible are called gen AI hallucinations. They are very confidently delivered, making it hard to spot or detect without thorough external verification. Standard model errors, on the other hand, are deviations from the model’s expected behavior (like incorrect classifications). Not all errors are hallucinations.
2. How does hallucination frequency vary across different generative AI models?
The rate of hallucination varies depending on a particular generative AI model’s architecture, training dataset, and domain complexity.
3. Which industries are most impacted by AI hallucinations?
Gen AI hallucinations impact the critical and highly regulated sectors like healthcare, finance, education, and legal the most as the risks can directly impact people and factual accuracy is a non-negotiable.
4. What are the best practices to reduce hallucination frequency in LLMs?
Ensuring the usage of high-quality datasets, implementation of RAG, appointment of human beings for periodic reviews (Human-in-the-Loop), and strict governance all come together in reducing hallucination frequency in large language models.
5. What are common causes of AI hallucinations in large language models?
Key causes include biased/incomplete training data, inadequate contextual grounding, overfitting to language patterns without factual verification, and insufficient model fine-tuning.
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Editorial Team
Tredence
