Fine-Grained Detection of Context-Grounded Hallucinations Using LLMs
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.
68e00ca373e20ab577841a58
Yehonatan Peisakhovsky
68e00ca373e20ab577841a59
Zorik Gekhman
claimed_verified
2025-10-05T12:46:18.114Z
61499a8f8517f77f09a3a990
/avatars/fa15d117e8ce8c50669455d3e483408b.svg
Zorik
user
zorik
68e00ca373e20ab577841a5a
Yosi Mass
68e00ca373e20ab577841a5b
Liat Ein-Dor
68e00ca373e20ab577841a5c
Roi Reichart
Fine-Grained Detection of Context-Grounded Hallucinations Using LLMs
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.
A study evaluates the effectiveness of large language models in identifying context-grounded hallucinations using a newly constructed benchmark and free-form textual descriptions, revealing challenges in distinguishing between missing details and unverifiable information.
LLMs context-grounded hallucinations meta-evaluation human annotation F1 score prompting strategies parametric knowledge