  Hedges allow speakers to mark utterances as provisional, whether to signal
non-prototypicality or "fuzziness", to indicate a lack of commitment to an
utterance, to attribute responsibility for a statement to someone else, to
invite input from a partner, or to soften critical feedback in the service of
face-management needs. Here we focus on hedges in an experimentally
parameterized corpus of 63 Roadrunner cartoon narratives spontaneously produced
from memory by 21 speakers for co-present addressees, transcribed to text
(Galati and Brennan, 2010). We created a gold standard of hedges annotated by
human coders (the Roadrunner-Hedge corpus) and compared three LLM-based
approaches for hedge detection: fine-tuning BERT, and zero and few-shot
prompting with GPT-4o and LLaMA-3. The best-performing approach was a
fine-tuned BERT model, followed by few-shot GPT-4o. After an error analysis on
the top performing approaches, we used an LLM-in-the-Loop approach to improve
the gold standard coding, as well as to highlight cases in which hedges are
ambiguous in linguistically interesting ways that will guide future research.
This is the first step in our research program to train LLMs to interpret and
generate collateral signals appropriately and meaningfully in conversation.
