@inproceedings{ventura-etal-2025-instruction,
    title = "Instruction-tuned {Q}wen{C}hart for Chart Question Answering",
    author = "Ventura, Viviana  and
      Kleybolte, Lukas Amadeus  and
      Zarcone, Alessandra",
    editor = "Ghosal, Tirthankar  and
      Mayr, Philipp  and
      Singh, Amanpreet  and
      Naik, Aakanksha  and
      Rehm, Georg  and
      Freitag, Dayne  and
      Li, Dan  and
      Schimmler, Sonja  and
      De Waard, Anita",
    booktitle = "Proceedings of the Fifth Workshop on Scholarly Document Processing (SDP 2025)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/tal-24-ingestion/2025.sdp-1.22/",
    doi = "10.18653/v1/2025.sdp-1.22",
    pages = "240--251",
    ISBN = "979-8-89176-265-7",
    abstract = "Charts, where information is delivered holistically by visual and textual features, represent a challenge when it comes to downstream tasks such as chart question answering, where both kinds of information contribute to the task. The standard approach is to decouple the task in two steps, first extracting information from the charts, or representing it as a table, text or code, and then a second reasoning step to output the answers. Today, the advancements in visual encoding of Visual Large Language Models (VLLM) have shown their capabilities to solve such complex tasks without using in-between representations of the charts or massive in-domain training. Our new instruction fine-tuned and chain-of-thought model QwenChart showed that even in a complex new benchmark such as SciVQA general models can achieve great performances with low-cost training, matching the capabilities that LLMs have showed in unimodal downstream tasks. An out-of-domain evaluation showed satisfactory results, albeit with an expected drop in performance."
}