LLMs can analyze and summarize large documents efficiently, making them valuable for tasks like report generation or content review. They process the input text to identify key themes, important points, and relevant details, enabling concise summaries that retain the core information. For instance, an LLM can take a lengthy research paper and generate a short summary highlighting the main findings.
Developers use LLMs for tasks like summarizing legal documents, creating executive summaries, or even condensing meeting transcripts. Pre-trained models can handle generic content, while fine-tuned models excel in domain-specific tasks. For example, a fine-tuned LLM might summarize medical records or financial statements with higher accuracy.
The ability to handle context and relationships in text makes LLMs effective for summarization. However, their performance can depend on the length and complexity of the input. Developers often preprocess the text, such as breaking it into manageable sections, to optimize results. Despite some limitations, LLMs significantly reduce the time required for manual summarization.