Key takeaways from the blog
Automation solutions having AI-powered conversational interfaces, such as Copilots, tend to hallucinate at times. The phenomenon mainly occurs when the prompt questions are not crisp, direct, and to-the-point. Lengthy and ambiguous prompts almost always pose the risk of inaccurate responses or hallucinations. Only a knowledgeable and well-informed person in the particular domain can identify and isolate such instances of AI-hallucinations. However, data sanctity and data reliability issues lie at the very core of the hallucinations. These issues need to be handled right at the inception of the AI-models. Otherwise, these issues assume disproportionate importance over time and breed bias. As a result, investments in technology, such as Copilot, that can be harnessed to improve business efficiency and productivity can become futile.
Copilot solutions or any Large Language Model (LLM) based solutions that are not grounded enough in the enterprise data sometimes make up natural language answers that seem correct but are not factual. The underlying model architecture, inference strategies, and pattern misinterpretation, while generating answers where direct answers are unavailable related to the query and the training data, are the main causes of hallucinations. Copilot solutions powered by more narrowly focused models (often called Small Language Models or SLMs) tend to provide more specific answers. They are probably less prone to hallucinations—but they are not immune altogether. Having said that model fine-tuning and using high-quality data though significantly reduce hallucinations, AI Copilots can throw up inaccuracies at certain times, albeit less frequently. Thus, AI-hallucinations remain an active area of interest and research.
The best option for reducing hallucinations in Conversational Interface solutions, such as Copilots, is training and retraining the reference data in the Language Model using continuous feedback loops. Some of the steps involved are –
Copilot solutions automate processes with ease to improve business productivity and efficiency. However, the AI-models and the underpinning training data pose a make-or-break difference. Poorly trained AI-models and insufficient model training data cause AI-hallucinations that render the Copilot investment futile. Businesses have to harness model training and re-training based on high-quality data right from inception to reduce hallucinations.
Disclaimer: While organizations can implement strategies to reduce hallucinations, they might not eliminate them completely. Frontier research in the areas of AI-models and AI-hallucinations, continuously improve the AI-models with better outcomes on each interaction and feedback to the Copilot solution. Continuous training and re-training of the models with real-time feedback loops remain the most acceptable way around AI-hallucinations.