Localizing factual inconsistencies in attributable text generation

Arie Cattan, Paul Roit, Shiyue Zhang, David Wan, Roee Aharoni, Idan Szpektor, Mohit Bansal, Ido Dagan

Abstract:

There has been an increasing interest in detecting hallucinations in model-generated texts,
both manually and automatically, at varying
levels of granularity. However, most existing
methods fail to precisely pinpoint the errors. In
this work, we introduce QASEMCONSISTENCY,
a new formalism for localizing factual inconsistencies in attributable text generation,
at a fine-grained level. Drawing inspiration
from Neo-Davidsonian formal semantics, we
propose decomposing the generated text into
minimal predicate-argument level propositions, expressed as simple question-answer
(QA) pairs, and assess whether each individual QA pair is supported by a trusted
reference text. As each QA pair corresponds to
a single semantic relation between a predicate
and an argument, QASEMCONSISTENCY effectively localizes the unsupported information.
We first demonstrate the effectiveness of the
QASEMCONSISTENCY methodology for human
annotation, by collecting crowdsourced annotations of granular consistency errors, while
achieving a substantial inter-annotator agreement. This benchmark includes more than 3K
instances spanning various tasks of attributable
text generation. We also show that QASEMCONSISTENCY yields factual consistency scores
that correlate well with human judgments.
Finally, we implement several methods for
automatically detecting localized factual inconsistencies, with both supervised entailment
models and LLMs.