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
KaPQA: Knowledge-Augmented Product Question-Answering
Swetha Eppalapally, Daksh Dangi, Chaithra Bhat, Ankita Gupta, Ruiyi Zhang, Shubham Agarwal, Karishma Bagga, Seunghyun Yoon, Nedim Lipka, Ryan A. Rossi, Franck Dernoncourt
OMoS-QA: A Dataset for Cross-Lingual Extractive Question Answering in a German Migration Context
Steffen Kleinle, Jakob Prange, Annemarie Friedrich
Bridging the Gap Between Information Seeking and Product Search Systems: Q&A Recommendation for E-commerce
Saar Kuzi, Shervin Malmasi
SPIQA: A Dataset for Multimodal Question Answering on Scientific Papers
Shraman Pramanick, Rama Chellappa, Subhashini Venugopalan
CompAct: Compressing Retrieved Documents Actively for Question Answering
Chanwoong Yoon, Taewhoo Lee, Hyeon Hwang, Minbyul Jeong, Jaewoo Kang
One Stone, Four Birds: A Comprehensive Solution for QA System Using Supervised Contrastive Learning
Bo Wang, Tsunenori Mine