Google BARD, announced this week, also tries and fails to perform attribution and verification: https://old.reddit.com/r/Bard/comments/11yeegu/google_bard_claims_bard_has_a...
BARD also produces lengthy passages from its training data verbatim without elicitation: https://old.reddit.com/r/Bard/comments/11xxaxj/bard_copied_user_text_from_a_...
.... Another thing the Foundation could do without editors getting involved (a class action suit by editors would probably at best be counterproductive at this point, for a number of reasons, and could backfire) is to highlight and encourage the ongoing but relatively obscure work on attribution and verification by LLMs. There are two projects in particular, SPARROW [ https://arxiv.org/abs/2209.14375 ] and RARR [https://arxiv.org/abs/2210.08726 ] that deserve wider recognition, support, and work on replication by third parties. These research directions are the most robust way to avoid the hallucination problems which are at the root of most everything that can go wrong when LLMs are used to produce Wikipedia content, so it would be extremely helpful if the Foundation uses its clout to shine a light and point out that they do what we expect of Wikipedia editors: provide sources in support of summary text cited in a way that third parties can independently verify.
The Bing LLM already includes some attempt at doing this with a dual process search system, which I believe is modeled after the SPARROW approach, but without the explicit rigor such as in RARR, it can fail spectacularly, and produce the same confidently wrong output everyone has recently become familiar with, but with the confounding problem of appearing to cite sources in support, but which aren't. For example, see this thread: https://twitter.com/dileeplearning/status/1634699315582226434
-LW