Question Answering
Question answering (QA) research aims to develop systems that accurately and efficiently respond to diverse questions posed in natural language. Current efforts focus on improving the robustness and efficiency of QA models, particularly in handling long contexts, ambiguous queries, and knowledge conflicts, often leveraging large language models (LLMs) and retrieval-augmented generation (RAG) architectures. These advancements are significant for various applications, including information retrieval, conversational AI, and educational tools, driving improvements in both the accuracy and accessibility of information.
Papers
MapQA: A Dataset for Question Answering on Choropleth Maps
Shuaichen Chang, David Palzer, Jialin Li, Eric Fosler-Lussier, Ningchuan Xiao
Reasoning Circuits: Few-shot Multihop Question Generation with Structured Rationales
Saurabh Kulshreshtha, Anna Rumshisky
Generative Long-form Question Answering: Relevance, Faithfulness and Succinctness
Dan Su
Empowering Language Models with Knowledge Graph Reasoning for Question Answering
Ziniu Hu, Yichong Xu, Wenhao Yu, Shuohang Wang, Ziyi Yang, Chenguang Zhu, Kai-Wei Chang, Yizhou Sun