@inproceedings{malviya-katsigiannis-2024-sk,
    title = "{SK}{\_}{DU} Team: Cross-Encoder based Evidence Retrieval and Question Generation with Improved Prompt for the {AV}eri{T}e{C} Shared Task",
    author = "Malviya, Shrikant  and
      Katsigiannis, Stamos",
    editor = "Schlichtkrull, Michael  and
      Chen, Yulong  and
      Whitehouse, Chenxi  and
      Deng, Zhenyun  and
      Akhtar, Mubashara  and
      Aly, Rami  and
      Guo, Zhijiang  and
      Christodoulopoulos, Christos  and
      Cocarascu, Oana  and
      Mittal, Arpit  and
      Thorne, James  and
      Vlachos, Andreas",
    booktitle = "Proceedings of the Seventh Fact Extraction and VERification Workshop (FEVER)",
    month = nov,
    year = "2024",
    address = "Miami, Florida, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2024.fever-1.11/",
    doi = "10.18653/v1/2024.fever-1.11",
    pages = "99--107",
    abstract = "As part of the AVeriTeC shared task, we developed a pipelined system comprising robust and finely tuned models. Our system integrates advanced techniques for evidence retrieval and question generation, leveraging cross-encoders and large language models (LLMs) for optimal performance. With multi-stage processing, the pipeline demonstrates improvements over baseline models, particularly in handling complex claims that require nuanced reasoning by improved evidence extraction, question generation and veracity prediction. Through detailed experiments and ablation studies, we provide insights into the strengths and weaknesses of our approach, highlighting the critical role of evidence sufficiency and context dependency in automated fact-checking systems. Our system secured a competitive rank, 7th on the development and 12th on the test data, in the shared task, underscoring the effectiveness of our methods in addressing the challenges of real-world claim verification."
}