One of the most common use cases of LLMs is summary generation. I have worked on multiple systems where we have summarized different kinds of documents – word, pdf, text, web pages, call transcripts, and video transcripts. I am building Videocrawl where we generate summaries of video content. In almost all the summary use cases I have implemented, we have gone with a static summary prompt where we instruct the LLM to generate a summary in a specific format. In my recent work, I have been playing with the idea of giving some agency to the summarizer so that we can generate dynamic summarization prompts. In this short post, I will share my approach.
Let’s make it concrete. Let’s assume that we want to summarize Search R1 paper. This paper covers how we can train LLMs to reason and leverage search engines for reinforcement learning.
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