How to Summarize Large Documents with LangChain and OpenAI
We’re so glad you’re here. You can expect all the best TNS content to arrive Monday through Friday to keep you on top of the news and at the top of your game.
Large language models have made many tasks easier like making chatbots, language translation, text summarization, etc. We used to write models for summarization, and then there was always the issue of performance. Now, we can do this easily with the use of large language models (LLMs). For example, state-of-the-art (SOTA) LLMs can already handle a whole book in its context window. But there are still some limitations when summarizing very large documents.
Contextual limit or context length in an LLM refers to the number of tokens that a model can process. Each model has its own context length also known as max tokens or token limit. For instance, a standard GPT-4 model has a context length of 128,000 tokens. It will lose information for the tokens more than that. Some SOTA LLMs have a contextual limit of up to 1 million tokens. However, as the contextual limit increases, LLMs suffer from limitations like recency and primacy. We can also delve into ways to mitigate these effects.
Both effects bias the model toward specific parts of the input data. The model may skip important information in the middle of the sequence.
