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
A Knowledge-Injected Curriculum Pretraining Framework for Question Answering
Xin Lin, Tianhuang Su, Zhenya Huang, Shangzi Xue, Haifeng Liu, Enhong Chen
InfiBench: Evaluating the Question-Answering Capabilities of Code Large Language Models
Linyi Li, Shijie Geng, Zhenwen Li, Yibo He, Hao Yu, Ziyue Hua, Guanghan Ning, Siwei Wang, Tao Xie, Hongxia Yang
Large Language Models(LLMs) on Tabular Data: Prediction, Generation, and Understanding -- A Survey
Xi Fang, Weijie Xu, Fiona Anting Tan, Jiani Zhang, Ziqing Hu, Yanjun Qi, Scott Nickleach, Diego Socolinsky, Srinivasan Sengamedu, Christos Faloutsos
JMLR: Joint Medical LLM and Retrieval Training for Enhancing Reasoning and Professional Question Answering Capability
Junda Wang, Zhichao Yang, Zonghai Yao, Hong Yu
Rainbow Teaming: Open-Ended Generation of Diverse Adversarial Prompts
Mikayel Samvelyan, Sharath Chandra Raparthy, Andrei Lupu, Eric Hambro, Aram H. Markosyan, Manish Bhatt, Yuning Mao, Minqi Jiang, Jack Parker-Holder, Jakob Foerster, Tim Rocktäschel, Roberta Raileanu
PAQA: Toward ProActive Open-Retrieval Question Answering
Pierre Erbacher, Jian-Yun Nie, Philippe Preux, Laure Soulier
Chain-of-Discussion: A Multi-Model Framework for Complex Evidence-Based Question Answering
Mingxu Tao, Dongyan Zhao, Yansong Feng
PerLTQA: A Personal Long-Term Memory Dataset for Memory Classification, Retrieval, and Synthesis in Question Answering
Yiming Du, Hongru Wang, Zhengyi Zhao, Bin Liang, Baojun Wang, Wanjun Zhong, Zezhong Wang, Kam-Fai Wong