  Claim verification is a task that involves assessing the truthfulness of a
given claim based on multiple evidence pieces. Using large language models
(LLMs) for claim verification is a promising way. However, simply feeding all
the evidence pieces to an LLM and asking if the claim is factual does not yield
good results. The challenge lies in the noisy nature of both the evidence and
the claim: evidence passages typically contain irrelevant information, with the
key facts hidden within the context, while claims often convey multiple aspects
simultaneously. To navigate this "noisy crowd" of information, we propose EACon
(Evidence Abstraction and Claim Deconstruction), a framework designed to find
key information within evidence and verify each aspect of a claim separately.
EACon first finds keywords from the claim and employs fuzzy matching to select
relevant keywords for each raw evidence piece. These keywords serve as a guide
to extract and summarize critical information into abstracted evidence.
Subsequently, EACon deconstructs the original claim into subclaims, which are
then verified against both abstracted and raw evidence individually. We
evaluate EACon using two open-source LLMs on two challenging datasets. Results
demonstrate that EACon consistently and substantially improve LLMs' performance
in claim verification.
