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_already_been_shut/jd77wpo/

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_forum_word_for_word/jd58764/

> .... 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
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