Tech

Day 88 of 100 Days of AI

Michael Tefula

Michael Tefula

— 1 min read

This evening I experimented with Langchain’s summarisation frameworks. This is something LLMs are great at natively, but with Langchain, you can use even more sophisticated summarisation techniques. Here’s a GPT-generated summary of these techniques based on the Langchain documentation:

From ChatGPT4o:

1. Stuff Method

  • Concept: Simply concatenate all documents into a single prompt and pass that prompt to a language model (LLM).
  • Usage:
    • Suitable for cases where the combined document size does not exceed the model’s token limit.
    • Useful for quick and simple summarization tasks.
  • Pros: Easy to implement.
  • Cons: Limited by the token capacity of the LLM; not efficient for large sets of documents.

2. Map-Reduce Method

  • Concept: A two-step approach where documents are first summarized individually (map), and then these summaries are combined into a final summary (reduce).
  • Usage:
    • Appropriate for summarizing large collections of documents.
    • Effective when documents are too large to be processed in a single prompt.
  • Pros: Can handle larger datasets by breaking them down into smaller chunks.
  • Cons: More complex to implement compared to the stuff method; may require tuning to balance between map and reduce stages.

3. Refine Method

  • Concept: Iteratively updates a summary by passing through the documents sequentially, refining the summary at each step.
  • Usage:
    • Best for situations where the documents can provide additional context sequentially.
    • Useful for creating a more detailed and nuanced summary.
  • Pros: Produces a progressively refined and detailed summary.
  • Cons: Can be time-consuming and computationally expensive due to iterative nature.

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