How do you handle hallucination or omission risks when summarizing long bills with LLMs? Is there any automated diffing or traceability back to specific sections of the source text?
Good question! I almost don't get problems with hallucinations. The worst case I had was oversimplification. I'm using mostly heuristic models, so they don't overthink; they just rely more on the source. If something is wrong, they usually mess up json, and it doesn't get through. Bills are typically long because of exposes, analyses, and predictions attached. I don't use it, as I'm focusing just on context sterilization and compression of info of the actual bill, not what it could be. Diffing would be wonderful! I have to think about it, thanks!