@inproceedings{kadam-ferraro-2025-tag,
    title = "{TAG}{--}{EQA}: Text{--}And{--}Graph for Event Question Answering via Structured Prompting Strategies",
    author = "Kadam, Maithili Sanjay  and
      Ferraro, Francis",
    editor = "Frermann, Lea  and
      Stevenson, Mark",
    booktitle = "Proceedings of the 14th Joint Conference on Lexical and Computational Semantics (*SEM 2025)",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.starsem-1.24/",
    pages = "304--315",
    ISBN = "979-8-89176-340-1",
    abstract = "Large language models (LLMs) excel at general language tasks but often struggle with event-based questions{---}especially those requiring causal or temporal reasoning. We introduce TAG-EQA (Text-And-Graph for Event Question Answering), a prompting framework that injects causal event graphs into LLM inputs by converting structured relations into natural-language statements. TAG-EQA spans nine prompting configurations, combining three strategies (zero-shot, few-shot, chain-of-thought) with three input modalities (text-only, graph-only, text+graph), enabling a systematic analysis of when and how structured knowledge aids inference. On the TORQUESTRA benchmark, TAG-EQA improves accuracy by {\textasciitilde}5{\%} on average over text-only baselines, with gains up to {\textasciitilde}12{\%} in zero-shot settings and {\textasciitilde}18{\%} when graph-augmented CoT prompting is effective. While performance varies by model and configuration, our findings show that causal graphs can enhance event reasoning in LLMs without fine-tuning, offering a flexible way to encode structure in prompt-based QA."
}