The First Place Solution of WSDM Cup 2024: Leveraging Large Language
  Models for Conversational Multi-Doc QA
Yiming Li Zhao Zhang
Conversational multi-doc question answering aims to answer specific questions
based on the retrieved documents as well as the contextual conversations. In
this paper, we introduce our winning approach for the "Conversational Multi-Doc
QA" challenge in WSDM Cup 2024, which exploits the superior natural language
understanding and generation capability of Large Language Models (LLMs). We
first adapt LLMs to the task, then devise a hybrid training strategy to make
the most of in-domain unlabeled data. Moreover, an advanced text embedding
model is adopted to filter out potentially irrelevant documents and several
approaches are designed and compared for the model ensemble. Equipped with all
these techniques, our solution finally ranked 1st place in WSDM Cup 2024,
surpassing its rivals to a large extent. The source codes have been released at
https://github.com/zhangzhao219/WSDM-Cup-2024.