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
FairytaleQA Translated: Enabling Educational Question and Answer Generation in Less-Resourced Languages
Bernardo Leite, Tomás Freitas Osório, Henrique Lopes Cardoso
M-QALM: A Benchmark to Assess Clinical Reading Comprehension and Knowledge Recall in Large Language Models via Question Answering
Anand Subramanian, Viktor Schlegel, Abhinav Ramesh Kashyap, Thanh-Tung Nguyen, Vijay Prakash Dwivedi, Stefan Winkler
Ask-EDA: A Design Assistant Empowered by LLM, Hybrid RAG and Abbreviation De-hallucination
Luyao Shi, Michael Kazda, Bradley Sears, Nick Shropshire, Ruchir Puri
Graph Neural Network Enhanced Retrieval for Question Answering of LLMs
Zijian Li, Qingyan Guo, Jiawei Shao, Lei Song, Jiang Bian, Jun Zhang, Rui Wang
LOVA3: Learning to Visual Question Answering, Asking and Assessment
Henry Hengyuan Zhao, Pan Zhou, Difei Gao, Mike Zheng Shou
Perception of Knowledge Boundary for Large Language Models through Semi-open-ended Question Answering
Zhihua Wen, Zhiliang Tian, Zexin Jian, Zhen Huang, Pei Ke, Yifu Gao, Minlie Huang, Dongsheng Li