Data cleaning—the often tedious process of fixing errors and inconsistencies in datasets—is a major bottleneck for data analysts. Could large language models (LLMs) be the key to automating this crucial task? New research explores this possibility with Cocoon, a system that uses LLMs to understand the *meaning* of data and clean it more effectively than traditional rule-based systems. Instead of relying on statistical anomalies alone, Cocoon mimics the human cleaning process by breaking down complex cleaning operations into smaller, more manageable steps. For example, instead of just flagging outliers based on numerical thresholds, Cocoon uses LLMs to understand if values like "N/A" or "null" actually represent missing data, regardless of their format. It can even recognize and correct inconsistent string representations, like converting "New York" and "NY" to a standardized format. Experiments show that Cocoon outperforms existing data cleaning tools on several benchmark datasets, achieving higher accuracy in identifying and fixing errors. However, the research also highlights the challenge of using LLMs with large datasets. Current LLMs have limited input capacity, so Cocoon uses statistical profiling to summarize the data before feeding it to the LLM. The complexity of data cleaning also requires breaking the task down into manageable components, mirroring how human analysts approach the problem. While promising, the research also points to limitations. For instance, LLMs can struggle with ambiguous data quality issues, like inconsistent timestamps in a flight dataset, where preserving the uncertainty might be more important than enforcing consistency. Future research directions include incorporating domain-specific knowledge to improve accuracy and developing more sophisticated error classification techniques to further automate the data cleaning workflow.