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
FoRAG: Factuality-optimized Retrieval Augmented Generation for Web-enhanced Long-form Question Answering
Tianchi Cai, Zhiwen Tan, Xierui Song, Tao Sun, Jiyan Jiang, Yunqi Xu, Yinger Zhang, Jinjie Gu
Benchmarking Open-Source Language Models for Efficient Question Answering in Industrial Applications
Mahaman Sanoussi Yahaya Alassan, Jessica López Espejel, Merieme Bouhandi, Walid Dahhane, El Hassane Ettifouri
RepLiQA: A Question-Answering Dataset for Benchmarking LLMs on Unseen Reference Content
Joao Monteiro, Pierre-Andre Noel, Etienne Marcotte, Sai Rajeswar, Valentina Zantedeschi, David Vazquez, Nicolas Chapados, Christopher Pal, Perouz Taslakian
Boosting Scientific Concepts Understanding: Can Analogy from Teacher Models Empower Student Models?
Siyu Yuan, Cheng Jiayang, Lin Qiu, Deqing Yang
Zero-Shot End-To-End Spoken Question Answering In Medical Domain
Yanis Labrak, Adel Moumen, Richard Dufour, Mickael Rouvier
MedREQAL: Examining Medical Knowledge Recall of Large Language Models via Question Answering
Juraj Vladika, Phillip Schneider, Florian Matthes
MrRank: Improving Question Answering Retrieval System through Multi-Result Ranking Model
Danupat Khamnuansin, Tawunrat Chalothorn, Ekapol Chuangsuwanich