Table of Contents
- The Impact of Generative AI on Enterprises
- The Challenge of AI Hallucination
Understanding AI Hallucination
- Definition and Explanation
- Types of AI Hallucinations
Common Causes of AI Hallucinations
- DInsufficient or Biased Training Data
- Overfitting
- Model Architecture Flaws
- Complex Tasks in a Single LLM Call
Strategies to Reduce AI Hallucinations
- Improving Training Data Quality
- Fine-Tuning and Reinforcement Learning
- Implementing Fact-Checking Mechanisms
- Leveraging User Feedback
- Using Evaluation Metrics (ROUGE, BLEU)
Generative AI has changed the way enterprises deal with data, workflow automation, and processing documents, etc. Businesses are constantly leveraging generative AI models to make their work easier with its capabilities. However, generative AI is booming everywhere; it comes with its own challenge. One of the most pressing challenges is AI hallucination. These are instances where the model provides output that is factually incorrect and is not sensible.
AI hallucinations cause serious problems for businesses from leading to misinformation to reputational damage. It also creates trust issues among customers, legal risks and compliance concern for businesses. For instance, a customer service chatbot in a financial institution when asked about a loan eligibility requirement, confidently provides incorrect information about interest rates or required documents. This would damage bank reputation and customer dissatisfaction.
What is AI Hallucination and Their Types
AI hallucinations occur when a generative AI model perceives patterns that are not existing to human imperceptibility. Through these patterns they create output that does not make any sense or which is inaccurate.
Generally, when we prompt to gen AI models, it provides correct answer by addressing the prompt. But sometimes, it hallucinates meaning it tend to provide wrong output that are not based on training data and are incorrectly decoded by the transformer.
There are majorly two types of AI hallucinations,
Intrinsic hallucination – The generated output will have answers contradicting to the source material. This is also called as manipulated information. For example, if we ask the model who went first on mars, it will provide the answer as Neil Armstrong, as the model almost know that the Neil Armstrong is the first person to reach moon and not mars. Hence this is a manipulated answer.
Extrinsic hallucination – Extrinsic hallucination, is when an LLM model generates information that is not supported by the source information. The output sounds reasonable but are not actually based on any provided reference data.
For example, an AI model summarizes a research paper on climatic conditions and mentions about temperature rise and carbon emissions. However, the summary included a statement like, polar bear population is declining due to climatic changes. This is called extrinsic hallucination, which the research paper is not suggesting but it is an additional information not added in the source document.
Common causes for Gen AI Model hallucinations
1. Insufficient or Biased training data: For AI models to perform well and provide accurate information it needs to be trained with high quality data. If the datasets are, incomplete, inconsistent, inaccurate or biased it will provide wrong output.
For instance, if an ML model needs eight more parameters to make accurate prediction but three of the important ones are missing, the predictions won’t be accurate. Even if the AI model tries to guess those missing values, it will turn as NULL.
2. Overfitting – When an AI model continuously trained on datasets it might struggle to understand the new inputs. This simply means, AI model often memorize certain details too well, but it fails to identify or adapt to the new inputs given. This will create hallucinations in AI model.
For example, if an AI model predicting weather is trained only for summer climate, it will identify weather conditionals related to summer like hot, clear skies etc. If asked about winter, the prediction will be general like clear skies because the AI model has learned from general understanding. This is called overfitting where the AI model fails to adapt to new situations.
3. Model architecture – The design of an AI model architecture influences the ability to produce accurate information. For example, a customer service chatbot that is designed to respond to product queries. If not designed properly, the model can’t distinguish between different product categories rightly, resulting in hallucinations.
4. Complex tasks in one LLM call – Chances of hallucination, lots of things at once, extract information, break into multiple tasks or agents.
If a given prompt in LLM models asks for too many complex tasks to be conducted at one go, there are high chances the AI model may struggle to maintain the accuracy. Overloading LLM with multiple objectives like (summarization, analysis, predictions) will increase the risk of errors. To reduce the risk of hallucination, we can break the tasks into smaller steps or use multiple agents for performing various tasks.
Ways to Reduce AI Hallucinations
1. Better training data – Bad data quality leads to biased and wrong predictions. This can be tackled by training AI model with better quality data from varied sources. A good quality data is having all the criteria such as accuracy, consistency, reliability, completeness etc. Data quality can be maintained in various ways like,
a.Through data governance frameworks
Stop bad data from entering systems – Data entry applications should have mandatory fields that don’t allow users to bypass these fields. A data review policy will help in ensuring data cleanliness in this process.
b. Perform data quality checks at the entry level – Setup data quality check at the entry levels and stop bad data from getting into data pipelines
c. Perform regular data quality audits – Regular data quality audits by 3rd party organisations will ensure high governance practices in the organization
Prescience’s Data Sentinel solution helps check data quality issues for various data sources such as SQL DBs, CSV, Excel and flat files. It is designed for various user personas such as Data Steward, Data Scientist, Data Engineer, etc., each having their own data requirements.
2. Fine tuning and reinforcement learning – Reinforcement learning from human feedback, known as (RLHF) is a technique where human opinions are incorporated to AI models during the training process. This refines AI responses catering more to human evaluations. This technique brings AI-generated content with human expectations thus reducing bias and hallucinations.
3. Fact-Checking Mechanisms – Fact checking techniques helps AI models provide output more accurately which is not based on old data but cross check with external references to provide their output. Retrieval augmented generation (RAG) is a technique used to combine its own knowledge with real-time facts from reliable sources, making responses more accurate. This reduces mistakes and helps AI models to respond with factual correctness.
4. User Feedback: Constant user feedback makes AI model smarter over time. If an AI model is providing wrong answer users can report it. This feedback helps developers fix mistakes and fine-tune the AI to make it more accurate in the future.
5. Implementing Evaluation Metric – For LLM models’ evaluation there are several metrics used, such as ROUGE which stands for (Recall-oriented understudy for Gisting evaluation) a widely used metric for evaluating the quality of generated text, particularly for tasks like text summarization. It will compare the AI model generated text with human generated text for reference. BLEU (Bilingual evaluation understudy) score is another metric for automatically translate text from one language to the other.
Real-world hallucination example
A global manufacturing company wanted to query in natural language, without requiring an expertise in SQL. The team at Prescience Decision Solution helped by processing SQL queries through LLMs, sending them to database, fetching the data and then converting it to NLP format. This helped the company to ask questions in plain English and receive insights in the form of charts, tables, graphs, and text-based responses.
However, it created a challenge of hallucination, as when asked about a query, the model generated fabricated data with incorrect information. For example, if a user prompted for strategic importance of a “Risk Factor” column, which did not exist. But then, the LLM populated a column with incorrect values. This kind of hallucination can be tackled with custom prompts.
Conclusion
As generative AI models like LLMs are developed and are advancing day by day, it is important to address the trustworthiness and reliability of these models. Wrong and hallucinated AI predictions will destroy business and brand reputation. These hallucinations are often created out of biased or insufficient training data, overfitting, or faulty model architecture. To tackle these hallucinations enterprises should primarily focus on training AI model with high quality data. The other techniques involve, reinforcement learning, fact-checking mechanisms, and user feedback.
Prescience Decision Solutions navigates the complexities related to data science and analytics across various industries like sales, finance, e-commerce, marketing, etc. by delivering custom solutions that integrate intelligent models to overcome challenges while ensuring data quality, transparency, and scalability. Additionally, Prescience’s expertise in generative AI helps businesses with cutting-edge solutions including AI-driven automation, advanced natural language processing and so on.
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