LLM vs Other Models for Summarization
|
How does LLM compare to other models in summarizing complex texts? LLMs (Large Language Models) like GPT-3 have some advantages and disadvantages compared to other model architectures for complex text summarization: Advantages: • They can leverage the large context window to understand long documents in full and identify the most important information. This helps generate more comprehensive summaries. • They tend to generate more fluent and coherent summaries since they have been trained on a huge amount of text. • They are more generalized and can be fine-tuned to summarize texts from various domains and genres. • They can generate either abstractive or extractive summaries depending on the fine-tuning objective. Disadvantages: • They tend to hallucinate and generate incorrect or irrelevant details since they were not explicitly trained for summarization. • They struggle to condense information and generate concise summaries. They often reproduce parts of the original text. • They have lower factuality and accuracy compared to models specifically trained for summarization. In contrast, models specifically trained for summarization like transformer-based encoder-decoder architectures tend to: • Generate more concise and factually correct summaries since that was their training objective. • Be better at identifying and reproducing only the most salient portions of the text. • Have higher accuracy on metrics like ROUGE which measure summary quality. However, they typically have a shorter context window (512-1024 tokens) and thus struggle with longer and more complex texts. They also tend to be less generalized. So in summary, while LLMs have some advantages for complex text summarization due to their large context window, they also have drawbacks. Models specifically trained for summarization currently generate more accurate and concise summaries, though they struggle with longer texts. Hope this comparison helps! Let me know if you have any other questions. |