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
TAT-LLM: A Specialized Language Model for Discrete Reasoning over Tabular and Textual Data
Fengbin Zhu, Ziyang Liu, Fuli Feng, Chao Wang, Moxin Li, Tat-Seng Chua
CFMatch: Aligning Automated Answer Equivalence Evaluation with Expert Judgments For Open-Domain Question Answering
Zongxia Li, Ishani Mondal, Yijun Liang, Huy Nghiem, Jordan Boyd-Graber
ChatQA: Surpassing GPT-4 on Conversational QA and RAG
Zihan Liu, Wei Ping, Rajarshi Roy, Peng Xu, Chankyu Lee, Mohammad Shoeybi, Bryan Catanzaro
Instant Answering in E-Commerce Buyer-Seller Messaging using Message-to-Question Reformulation
Besnik Fetahu, Tejas Mehta, Qun Song, Nikhita Vedula, Oleg Rokhlenko, Shervin Malmasi
LiDAR-LLM: Exploring the Potential of Large Language Models for 3D LiDAR Understanding
Senqiao Yang, Jiaming Liu, Ray Zhang, Mingjie Pan, Zoey Guo, Xiaoqi Li, Zehui Chen, Peng Gao, Yandong Guo, Shanghang Zhang
Shai: A large language model for asset management
Zhongyang Guo, Guanran Jiang, Zhongdan Zhang, Peng Li, Zhefeng Wang, Yinchun Wang