LLMCheckup: Conversational Examination of Large Language Models via
  Interpretability Tools
Qianli Wang Tatiana Anikina Nils Feldhus Josef van Genabith Leonhard Hennig Sebastian Möller
Interpretability tools that offer explanations in the form of a dialogue have
demonstrated their efficacy in enhancing users' understanding, as one-off
explanations may occasionally fall short in providing sufficient information to
the user. Current solutions for dialogue-based explanations, however, require
many dependencies and are not easily transferable to tasks they were not
designed for. With LLMCheckup, we present an easily accessible tool that allows
users to chat with any state-of-the-art large language model (LLM) about its
behavior. We enable LLMs to generate all explanations by themselves and take
care of intent recognition without fine-tuning, by connecting them with a broad
spectrum of Explainable AI (XAI) tools, e.g. feature attributions,
embedding-based similarity, and prompting strategies for counterfactual and
rationale generation. LLM (self-)explanations are presented as an interactive
dialogue that supports follow-up questions and generates suggestions.
LLMCheckup provides tutorials for operations available in the system, catering
to individuals with varying levels of expertise in XAI and supports multiple
input modalities. We introduce a new parsing strategy called multi-prompt
parsing substantially enhancing the parsing accuracy of LLMs. Finally, we
showcase the tasks of fact checking and commonsense question answering.