Lobsters community discusses practical application of de-anthropomorphized terminology
2 Sep 22 · 4d ago · 1 post · 1 source · development 2 of 2
The research was shared on Lobsters where users discussed its utility for documentation and systems discussion. One commenter noted it was "very good to use for documentation of systems and tasks."
Inie et al. Researchers
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What people said 12 voices · verbatim
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I like "confabulation" over "hallucination" and I do use the term LLM over AI (as there's more to AI than just LLMs).
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I enjoy the article and its argument, but some of the proposed alternatives aren't nearly as catchy as what they try to replace, like "hallucination → undesirable output." I certainly agree with locating agency with people! For those interested, Melanie Mitchell has two articles critical of the misleading metaphors and anthropomorphizing…
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non-technical people really believe AI hacks companies on their own or find cancer cures by crunching numbers endlessly. This language is not for technical people who treat AI as matrix multiplications, it's for your grandma that knows about AI because Trump goes on TV saying it should be renamed "super intelligence".
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Since these technologies are used for stuff like committing genocides, mass displacement of workers, surveillance by authoritarian governments and other evil purposes, the stake of using such language is deflecting the attention from the perpetrators (tech oligarchs and governments) who want you to believe the machines are autonomous and…
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Oh, interesting find! I've thought about this a lot, but I still learned a few things from this article. I have heard good arguments in favour of anthropomorphisation, but I still want to be a little careful in my own writing. TFA misses a very common case: the verb "to write". In my head, an LLM cannot "write" anything, but it can "generate"…
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I don't know if avoiding using anthropomorphizing language is worth it. As a species, we're *great* at anthropomorphizing everything from roombas to computers to hulking lab equipment. Besides, some terms here are fairly uncontroversial or even ancient in computing terms - "speech recognition" was coined in the 60s.
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That's the thing, I don't see that language as "deflecting the attention from the perpetrators", and rather just another case of us referring to non-sentient things as sentient.
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For the reasoning part, you can use something like "multi-step generation" or "chained generation" or in general something that gives transparency that in the end is just generating text to embed into other prompts.
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"Generate" is good. I prefer "output" because no human ever would be described as "outputting" anything ever though I "generate" ideas often. For "reasoning" I would recommend "intermediate tokens" like Subbarao Kambhampati calls them: https://x.com/rao2z/status/1927707640223719631 For "explains" I use "indicates" since machines do that plenty and…
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An ASU professor calls them "intermediate tokens" and has built his career about CoR analysis:
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I like the concept and will take it with me. I think it's a healthy distinction to have and a casual, passive way to evangelise responsible AI discussion. However I think a lot of the examples in this could be improved upon and are not necessarily scoped to just being used within "AI". For example, I could substitute `numpy` in many of the cases…
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I think both could be true at the same time.
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