Check that quoted evidence really is in the document

ans$evidence says what an answer rests on. For most readers those spans are verbatim chunk text and are true by construction. For skim they are what the model chose to write when asked to extract the relevant passages. They are presented as quotations, and this is what checks that they are.

Usage

gr_verify_evidence(answer, chunks = NULL)

Arguments

answer

A gr_answer.

chunks

The gr_chunks the answer was read from. Needed for readers whose evidence is verbatim, where the comparison is against the chunk the span claims to come from. skim answers already carry their sources, so they can be checked without it; an ensemble needs it for the rows its verbatim members contributed, even though its skim rows do not.

Pass the chunks the answer was actually read from. Chunk ids are positional, so a different chunk set of the same size will match on id and compare each span against unrelated text, reporting verified = FALSE for evidence that is perfectly sound. An id the chunk set does not contain reports NA, because there was nothing to compare against.

Value

A data frame with one row per evidence span: chunk_id, kind ("verbatim", "extracted" or "answer", per row; an ensemble mixes them in one table, and the same value appears as the kind column on ans$evidence itself), verified, match and span (the first 60 characters). verified is NA where the question does not apply: a map_reduce evidence row is a per-chunk answer, not a quotation, and asking whether it appears in the chunk is a category error.

Details

A fabricated citation is more convincing than a fabricated answer, because it looks like the thing that would let you check. Verification is a string operation on text you already have, so it costs nothing and there is no reason not to do it.

What the numbers mean

match is 1 for an exact quotation once whitespace, quote marks, dashes and case are folded away. These are the differences a faithful quotation introduces. Below 1 it is the fraction of the span's words carried by its longest consecutive run in the source.

Read that number with its shape in mind. Because it measures a run, where the change falls matters as much as how much changed: altering the last word of a ten-word span leaves a run of nine and scores 0.9, while altering a word in the middle splits the span and scores about 0.5. So a mid-sentence change (a swapped figure, the case this exists to catch) lands near 0.5, not near 0.9. Below roughly 0.3 there is no quotation left at all, only shared vocabulary. A run measure is still the right one: word overlap cannot tell a quotation from a paraphrase assembled out of the same words.

Citations

With cite = TRUE a reader asks the model to mark its sources as [chunk 3]. Every answer is checked for citations pointing at chunks that were never sent, whatever this function is called with; the result is ans$notes$cited_unknown, and an answer carrying one is partial.

Examples

# A model that quotes faithfully.
honest <- gr_mock_client(function(messages, params) {
  txt <- messages[[length(messages)]]$content
  if (grepl("You extract evidence", messages[[1]]$content, fixed = TRUE)) {
    return("Revenue rose to 45.2 million dollars.")
  }
  "Revenue was 45.2 million dollars."
})

doc <- "Revenue rose to 45.2 million dollars.\n\nHeadcount grew to 1,204."
ch <- gr_segment(gr_ingest(doc), list(method = "paragraph", max_tokens = 40))
#> Ingested 2 block(s), ~26 tokens (0 chars removed by cleaning).
#> Segmenting with 'paragraph' (cap 40 tokens, overlap 0).
ans <- gr_read(ch, "What was revenue?", honest, "skim")
#> Reading with 'skim' (all|N+1|none) over 1 chunk(s).
gr_verify_evidence(ans)
#>   chunk_id      kind verified match                                  span
#> 1        1 extracted     TRUE     1 Revenue rose to 45.2 million dollars.

# A model that invents one. The span is fluent, plausible, and not in the
# document. That is exactly the case a reader cannot catch by eye.
liar <- gr_mock_client(function(messages, params) {
  if (grepl("You extract evidence", messages[[1]]$content, fixed = TRUE)) {
    return("Revenue rose to 88.9 billion dollars on record demand.")
  }
  "Revenue was 88.9 billion dollars."
})
bad <- gr_read(ch, "What was revenue?", liar, "skim")
#> Reading with 'skim' (all|N+1|none) over 1 chunk(s).
gr_verify_evidence(bad)
#>   chunk_id      kind verified match
#> 1        1 extracted    FALSE 0.333
#>                                                     span
#> 1 Revenue rose to 88.9 billion dollars on record demand.
bad$partial
#> [1] TRUE