Large language models like GPT-4 have shown incredible capabilities, but they fall short when it comes to rigorous data analysis, especially on large datasets. This shortcoming arises from their relatively small context window sizes that cannot accommodate full datasets, as well as the probabilistic nature of their text generation process which is ill-suited for precise statistical calculations.

Even for a moderately-sized 66MB text corpus like the IMDB movie reviews, LLMs encounter significant obstacles. Their context windows force the use of small subdivided portions rather than the complete data. Furthermore, their generative approach yields hallucinated, statistically inaccurate results when run over the full dataset. Complex analysis tasks requiring multiple stages, like identifying top phrases and linguistic biases, prove extremely challenging for LLMs.

However, by strategically integrating LLMs with specialized text analysis tools designed for such tasks, one can harness the strengths of both. Tools like Wordview have the ability to process entire datasets rapidly and calculate precise statistics. When coupled with an LLM interface like GPT-3.5, users gain the power of natural language interaction to interpret and explore those analytical results, while ensuring the LLM’s outputs remain grounded in factual data rather than hallucinating. This combined approach unlocks LLMs’ potential for robust enterprise data analysis previously hindered by their limitations.