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
Evaluating Correctness and Faithfulness of Instruction-Following Models for Question Answering
Vaibhav Adlakha, Parishad BehnamGhader, Xing Han Lu, Nicholas Meade, Siva Reddy
KoBBQ: Korean Bias Benchmark for Question Answering
Jiho Jin, Jiseon Kim, Nayeon Lee, Haneul Yoo, Alice Oh, Hwaran Lee
No that's not what I meant: Handling Third Position Repair in Conversational Question Answering
Vevake Balaraman, Arash Eshghi, Ioannis Konstas, Ioannis Papaioannou
Investigating Prompting Techniques for Zero- and Few-Shot Visual Question Answering
Rabiul Awal, Le Zhang, Aishwarya Agrawal
Learning to Summarize and Answer Questions about a Virtual Robot's Past Actions
Chad DeChant, Iretiayo Akinola, Daniel Bauer
Clickbait Classification and Spoiling Using Natural Language Processing
Adhitya Thirumala, Elisa Ferracane
Benchmarking Foundation Models with Language-Model-as-an-Examiner
Yushi Bai, Jiahao Ying, Yixin Cao, Xin Lv, Yuze He, Xiaozhi Wang, Jifan Yu, Kaisheng Zeng, Yijia Xiao, Haozhe Lyu, Jiayin Zhang, Juanzi Li, Lei Hou
Gotta: Generative Few-shot Question Answering by Prompt-based Cloze Data Augmentation
Xiusi Chen, Yu Zhang, Jinliang Deng, Jyun-Yu Jiang, Wei Wang