Fine-Grained Detection of Context-Grounded Hallucinations Using LLMs
Authors: name: Yehonatan Peisakhovsky; name: Zorik Gekhman; name: Yosi Mass; name: Liat Ein-Dor; name: Roi Reichart
Published: 2025-09-26 17:03:24

Abstract: Context-grounded hallucinations are cases where model outputs contain
information not verifiable against the source text. We study the applicability
of LLMs for localizing such hallucinations, as a more practical alternative to
existing complex evaluation pipelines. In the absence of established benchmarks
for meta-evaluation of hallucinations localization, we construct one tailored
to LLMs, involving a challenging human annotation of over 1,000 examples. We
complement the benchmark with an LLM-based evaluation protocol, verifying its
quality in a human evaluation. Since existing representations of hallucinations
limit the types of errors that can be expressed, we propose a new
representation based on free-form textual descriptions, capturing the full
range of possible errors. We conduct a comprehensive study, evaluating four
large-scale LLMs, which highlights the benchmark's difficulty, as the best
model achieves an F1 score of only 0.67. Through careful analysis, we offer
insights into optimal prompting strategies for the task and identify the main
factors that make it challenging for LLMs: (1) a tendency to incorrectly flag
missing details as inconsistent, despite being instructed to check only facts
in the output; and (2) difficulty with outputs containing factually correct
information absent from the source - and thus not verifiable - due to alignment
with the model's parametric knowledge.