The Intrinsic Limitations of Large Language Models: Understanding Hallucinations and Their Impact on Data Workflows

Large Language Models (LLMs) have revolutionized natural language processing and opened new frontiers in data applications. However, they are not without limitations.

This talk will delve into the primary constraints of LLMs, focusing on the phenomenon of hallucinations—instances where models generate incorrect or nonsensical information. Contrary to common perception, these hallucinations are not mere bugs but an inherent feature of how LLMs are designed and trained : in other words, hallucinations will never disappear from LLMs even in 10 years. Besides, hallucinations are, by design of LLMs, very convincing, and sometimes hard to detect !

We will explore the underlying reasons for these limitations, rooted in the probabilistic and auto-regressive nature of LLMs. Understanding why hallucinations occur is crucial for recognizing that they cannot be entirely eliminated. Instead, they must be managed effectively, especially when integrating LLMs into data pipelines.

The talk will address the concrete implications of LLM limitations for data engineers, data analysts, and business users. We will examine scenarios where hallucinations can lead to data misinterpretation, flawed analysis, and erroneous business decisions.

Additionally, practical strategies for mitigating the impact of these limitations will be discussed, including model fine-tuning, incorporating human-in-the-loop approaches, and leveraging complementary technologies to enhance reliability.