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The government has a lot of text to process, and LLMs are good at processing text, and they can be implemented pretty safely in these roles. An obvious example
by kube-system 1y ago
The government has a lot of text to process, and LLMs are good at processing text, and they can be implemented pretty safely in these roles.
An obvious example might be: Someone who is trying to accomplish a task, but needs to verify the legal authorization/justification/guidelines etc to do that task. If they don't have the specific regulation memorized (e.g. the one person who was doing this esoteric task for 20 years just got laid off by DOGE) they may have to spend a lot of time searching legal texts. LLMs do a great job of searching texts in intuitive ways that traditional text searches can't.
- bigfishrunning 1y agoBut does the job of verifying that LLM output outweigh the job of just doing the search the old fashioned way? Probably, but we'll skip verification, just like always. This is the scariest feature of LLMs; Failure is built into the design of the system, but people just laugh and call the failures "hallucinations" and move on. The efficiency gains from AI come entirely from trusting a system that can't be trusted
- kube-system 1y agoNot all implementations of LLMs are "just type questions into chatgpt.com or ollama and trust the raw result", even though that is probably what people are most familiar with right now. They can be used pretty safely when incorporated into other systems that have guardrails on them. Not simply a dumb wrapper, but inside of systems that simply use LLMs as processing tools. For example, one extremely safe use case is using LLMs as a search tool. Ask it to cite its sources, then string match those sources back against the source texts. You are guaranteed that the sources actually exist, because you validated it.