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Progressive summarization works pretty well - I'm using that for https://findsight.ai https://findsight.ai You can even get lesser LLMs to do the bulk reductio
by summarity 4y ago
Progressive summarization works pretty well - I'm using that for https://findsight.ai https://findsight.ai
You can even get lesser LLMs to do the bulk reduction that have GPT clean it up on the way to even less content. Admittedly, that does take a lot of prompt engineering, chunk selection and reinforcement though (LLM supervising LLM).
- alchemist1e9 4y agoI understood everything except: > reinforcement though (LLM supervising LLM). Is there something I can read to understand what that looks like?
- summarity 4y agoI don't think this approach is formalized but I can give a few examples: A) Prompt leak prevention: chunk and embed LLM responses, than compare against original prompt to filter out chunks that leak the prompt B) Automatic prompt refinement: Prompt a cheap model, use an expensive model to judge the output and rewrite the prompt (this is in part how Vicuna[1] did eval for their LLaMa fine-tuning) Basically using LLMs in the feedback loop. [1] https://vicuna.lmsys.org https://vicuna.lmsys.org