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
Lexicalization Is All You Need: Examining the Impact of Lexical Knowledge in a Compositional QALD System
David Maria Schmidt, Mohammad Fazleh Elahi, Philipp Cimiano
MEG: Medical Knowledge-Augmented Large Language Models for Question Answering
Laura Cabello, Carmen Martin-Turrero, Uchenna Akujuobi, Anders Søgaard, Carlos Bobed
Enhancing Question Answering Precision with Optimized Vector Retrieval and Instructions
Lixiao Yang, Mengyang Xu, Weimao Ke
GRS-QA -- Graph Reasoning-Structured Question Answering Dataset
Anish Pahilajani, Devasha Trivedi, Jincen Shuai, Khin S. Yone, Samyak Rajesh Jain, Namyong Park, Ryan A. Rossi, Nesreen K. Ahmed, Franck Dernoncourt, Yu Wang
Show Me What and Where has Changed? Question Answering and Grounding for Remote Sensing Change Detection
Ke Li, Fuyu Dong, Di Wang, Shaofeng Li, Quan Wang, Xinbo Gao, Tat-Seng Chua
Dynamic Uncertainty Ranking: Enhancing In-Context Learning for Long-Tail Knowledge in LLMs
Shuyang Yu, Runxue Bao, Parminder Bhatia, Taha Kass-Hout, Jiayu Zhou, Cao Xiao
AAAR-1.0: Assessing AI's Potential to Assist Research
Renze Lou, Hanzi Xu, Sijia Wang, Jiangshu Du, Ryo Kamoi, Xiaoxin Lu, Jian Xie, Yuxuan Sun, Yusen Zhang, Jihyun Janice Ahn, Hongchao Fang, Zhuoyang Zou, Wenchao Ma, Xi Li, Kai Zhang, Congying Xia, Lifu Huang, Wenpeng Yin
NeuroSym-BioCAT: Leveraging Neuro-Symbolic Methods for Biomedical Scholarly Document Categorization and Question Answering
Parvez Zamil, Gollam Rabby, Md. Sadekur Rahman, Sören Auer
LongRAG: A Dual-Perspective Retrieval-Augmented Generation Paradigm for Long-Context Question Answering
Qingfei Zhao, Ruobing Wang, Yukuo Cen, Daren Zha, Shicheng Tan, Yuxiao Dong, Jie Tang
Leveraging the Domain Adaptation of Retrieval Augmented Generation Models for Question Answering and Reducing Hallucination
Salman Rakin, Md. A.R. Shibly, Zahin M. Hossain, Zeeshan Khan, Md. Mostofa Akbar
An Adaptive Framework for Generating Systematic Explanatory Answer in Online Q&A Platforms
Ziyang Chen, Xiaobin Wang, Yong Jiang, Jinzhi Liao, Pengjun Xie, Fei Huang, Xiang Zhao
Reverse Question Answering: Can an LLM Write a Question so Hard (or Bad) that it Can't Answer?
Nishant Balepur, Feng Gu, Abhilasha Ravichander, Shi Feng, Jordan Boyd-Graber, Rachel Rudinger
Evaluating Consistencies in LLM responses through a Semantic Clustering of Question Answering
Yanggyu Lee, Jihie Kim