💊 Pill of the week
In the previous issue, we explored the Stuff and Map Reduce chain methods in LangChain, both of which provide effective ways to handle text with large language models (LLMs). In this follow-up, we’ll take a closer look at the final two methods: Map Rerank and Refine.
These two advanced techniques offer additional flexibility and refinement, especially when quality control or iterative improvement is essential for processing larger or more complex inputs.
Have a look at the first two methods covered here:
Let's continue with the next two:
3. Map Rerank Chain
The Map Rerank chain is similar to the Map Reduce chain in that it involves processing chunks of text independently. However, instead of aggregating the results, this method involves scoring or ranking the outputs and selecting the best one.
How Map Rerank Chain Works
Mapping: The text is divided into chunks, and each chunk is processed independently by the model to generate candidate outputs.
Reranking: Each output is then evaluated or scored based on certain criteria (e.g., relevance, coherence). The highest-ranked output is selected as the final result.
When to Use Map Rerank Chain
The Map Rerank chain is best used when:
You want to ensure the quality of the final output by selecting the best option from multiple candidates.
The task involves generating alternative outputs where one needs to be chosen, such as in question answering or summarization tasks where precision is critical.
The input contains potentially irrelevant information, and you need to focus on the most pertinent parts.
Strengths of Map Rerank Chain
Quality Control: By reranking the outputs, this method ensures that the final result is the best possible among the candidates.
Focus on Relevance: It is particularly useful when relevance or accuracy is more important than producing a blended or averaged result.
Flexibility: The reranking criteria can be customized to suit specific task requirements.
Limitations of Map Rerank Chain
Computationally Intensive: Evaluating and scoring multiple outputs can be more computationally expensive and time-consuming than other methods.
Subjectivity in Scoring: The effectiveness of the reranking depends on the scoring criteria, which can sometimes be subjective or hard to define.
Potential for Information Loss: By selecting only one output, there's a risk of discarding potentially valuable information from other outputs.
4. Refine Chain
The Refine chain is an iterative method that refines the output over several passes. This method is particularly useful for refining a summary based on new context, offering a simpler alternative to the Map Reduce technique for handling large documents.
How Refine Chain Works
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