HILL: A Hallucination Identifier for Large Language Models
Proceedings of the CHI Conference on Human Factors in Computing Systems(2024)
摘要
Large language models (LLMs) are prone to hallucinations, i.e., nonsensical,
unfaithful, and undesirable text. Users tend to overrely on LLMs and
corresponding hallucinations which can lead to misinterpretations and errors.
To tackle the problem of overreliance, we propose HILL, the "Hallucination
Identifier for Large Language Models". First, we identified design features for
HILL with a Wizard of Oz approach with nine participants. Subsequently, we
implemented HILL based on the identified design features and evaluated HILL's
interface design by surveying 17 participants. Further, we investigated HILL's
functionality to identify hallucinations based on an existing
question-answering dataset and five user interviews. We find that HILL can
correctly identify and highlight hallucinations in LLM responses which enables
users to handle LLM responses with more caution. With that, we propose an
easy-to-implement adaptation to existing LLMs and demonstrate the relevance of
user-centered designs of AI artifacts.
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