> We further construct an open-domain document-level factuality benchmark in three-level granularity: claim, sentence and document A 2020 Meta paper [1] mentio

> We further construct an open-domain document-level factuality benchmark in three-level granularity: claim, sentence and document A 2020 Meta paper [1] mentions FEVER [2], which was published in 2018. [1] "Language models as fact checkers?" (2020) https://scholar.google.com/scholar?cites=3466959631133385664 https://scholar.google.com/scholar?cites=3466959631133385664 [2] https://paperswithcode.com/dataset/fever https://paperswithcode.com/dataset/fever I've collected various ideas for publishing premises as linked data; "#StructuredPremises" "#nbmeta" https://www.google.com/search?q=%22structuredpremises%22 https://www.google.com/search?q=%22structuredpremises%22 From "GenAI and erroneous medical references" https://news.ycombinator.com/item?id=39497333 https://news.ycombinator.com/item?id=39497333 : >> Additional layers of these 'LLMs' could read the responses and determine whether their premises are valid and their logic is sound as necessary to support the presented conclusion(s), and then just suggest a different citation URL for the preceding text > [...] "Find tests for this code" > "Find citations for this bias" From https://news.ycombinator.com/item?id=38353285 https://news.ycombinator.com/item?id=38353285 : > "LLMs cannot find reasoning errors, but can correct them" https://news.ycombinator.com/item?id=38353285 https://news.ycombinator.com/item?id=38353285 > "Misalignment and [...]"