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 perspectives in data applications. However, they are not without limitations.

This presentation will explore the main constraints of LLMs, focusing on the phenomenon of hallucinations—cases where models generate incorrect or absurd information. Contrary to common perception, these hallucinations are not simple bugs, but an inherent characteristic of how LLMs are designed and trained: in other words, hallucinations will never disappear from LLMs, even in 10 years. Moreover, hallucinations are, by design of LLMs, very convincing and sometimes difficult 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 to recognizing that they cannot be completely eliminated. They must rather be managed effectively, particularly when integrating LLMs into data pipelines. The presentation 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.

Furthermore, practical strategies to mitigate the impact of these limitations will be discussed, including model fine-tuning, integration of human-in-the-loop approaches, and the use of complementary technologies to improve reliability.

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