DynaQuest: A Dynamic Question Answering Dataset Reflecting Real-World Knowledge Updates (2025.findings-acl)
Copied to clipboard
| Challenge: | Large language models (LLMs) are typically trained on static datasets, preventing them from integrating real-time updates. |
| Approach: | They propose a dynamic question-answer answering dataset reflecting real-world knowledge updates that are automatically compared between Wikipedia versions and generating question-anchor pairs based on these updates. |
| Outcome: | The proposed framework improves LLMs' performance on time-sensitive question answering by maintaining a dynamic knowledge updating process. |
Similar Papers
Time-MQA: Time Series Multi-Task Question Answering with Context Enhancement (2025.acl-long)
Copied to clipboard
Yaxuan Kong, Yiyuan Yang, Yoontae Hwang, Wenjie Du, Stefan Zohren, Zhangyang Wang, Ming Jin, Qingsong Wen
| Challenge: | Existing time series models focus on a narrow spectrum of tasks, such as forecasting or anomaly detection. |
| Approach: | They propose a framework that enables natural language queries across multiple time series tasks such as numerical analytical tasks and open-ended question answering with reasoning. |
| Outcome: | The proposed framework enables natural language queries across multiple time series tasks and allows for more advanced and intuitive interactions with temporal data. |
Carpe diem: On the Evaluation of World Knowledge in Lifelong Language Models (2024.naacl-long)
Copied to clipboard
| Challenge: | Current language models are trained on static data, implying that the encoded knowledge could go wrong as time passes. |
| Approach: | They propose a temporally evolving question-answering benchmark for language models . they use Wikipedia databases to test language models for dynamic knowledge in ever-changing world . |
| Outcome: | The proposed task aims to model the evolution-adaptability of language models in the real world. |
DyKnow: Dynamically Verifying Time-Sensitive Factual Knowledge in LLMs (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Factual knowledge is subject to time-sensitive changes, and static benchmarks cannot address those cases. |
| Approach: | They propose to dynamically evaluate LLMs' knowledge and their time-sensitiveness against Wikidata, an up-to-date knowledge graph. |
| Outcome: | The proposed method compares LLMs and their time-sensitive knowledge against Wikidata, a publicly available up-to-date knowledge graph. |
Let LLMs Take on the Latest Challenges! A Chinese Dynamic Question Answering Benchmark (2025.coling-main)
Copied to clipboard
Zhikun Xu, Yinghui Li, Ruixue Ding, Xinyu Wang, Boli Chen, Yong Jiang, Haitao Zheng, Wenlian Lu, Pengjun Xie, Fei Huang
| Challenge: | Recent work has noted that due to the extremely high cost of iterative updates of LLMs, they are often unable to answer dynamic questions well. |
| Approach: | They propose a Chinese Dynamic QA benchmark containing question-answer pairs related to the latest dynamic questions on the Chinese Internet. |
| Outcome: | The proposed benchmark will be one of the key data resources for improving LLMs’ Chinese question-answering ability in the future. |
ComplexTempQA: A 100m Dataset for Complex Temporal Question Answering (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing datasets that focus on temporal knowledge are limited in size and lack comprehensive coverage of temporal information. |
| Approach: | They introduce a large-scale temporal question-answer-matching dataset . the new taxonomy categorizes questions as attributes, comparisons, and counting questions . |
| Outcome: | The proposed dataset surpasses existing benchmarks in scale and scope. |
Enhancing Temporal Sensitivity and Reasoning for Time-Sensitive Question Answering (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Existing language models have limited sensitivity to temporal information and inadequate temporal reasoning capabilities. |
| Approach: | They propose a framework that enhances temporal awareness and reasoning . they propose to use Temporal Information-Aware Embedding and Granular Contrastive Reinforcement Learning . |
| Outcome: | The proposed framework outperforms existing LLMs on time-sensitive question answering tasks. |
GrowOVER: How Can LLMs Adapt to Growing Real-World Knowledge? (2024.acl-long)
Copied to clipboard
| Challenge: | Existing knowledge-based datasets are outdated due to the rapid evolution of knowledge. |
| Approach: | They propose a retrieval-interactive language model framework that evaluates and reflects on its answers for further re-retrieval. |
| Outcome: | The proposed framework performs comparably to or surpasses continuously trained language models. |
NeuralQA: A Usable Library for Question Answering (Contextual Query Expansion + BERT) on Large Datasets (2020.emnlp-demos)
Copied to clipboard
| Challenge: | Existing tools for Question Answering (QA) have challenges that limit their use in practice. |
| Approach: | They propose a library that integrates with existing infrastructure and offers helpful defaults for QA subtasks. |
| Outcome: | NeuralQA integrates well with existing infrastructure and offers helpful defaults for QA subtasks. |
ComRAG: Retrieval-Augmented Generation with Dynamic Vector Stores for Real-time Community Question Answering in Industry (2025.acl-industry)
Copied to clipboard
| Challenge: | Existing methods for Community Question Answering (CQA) focus on static knowledge, limiting their applicability to real-world scenarios. |
| Approach: | They propose a retrieval-augmented generation framework for real-time industrial CQA that integrates static knowledge with dynamic historical QA pairs via a centroid-based memory mechanism. |
| Outcome: | The proposed framework outperforms baselines on three industrial CQA datasets and achieves 25.9% improvement in vector similarity, reducing latency by 8.7%–23.3%, and lowering chunk growth from 20.23% to 2.06% over iterations. |
UnSeenTimeQA: Time-Sensitive Question-Answering Beyond LLMs’ Memorization (2025.acl-long)
Copied to clipboard
Md Nayem Uddin, Amir Saeidi, Divij Handa, Agastya Seth, Tran Cao Son, Eduardo Blanco, Steven Corman, Chitta Baral
| Challenge: | UnSeenTimeQA is a data contamination-free time-sensitive question-answering benchmark. |
| Approach: | They propose a data contamination-free time-sensitive question-answering benchmark that avoids web-searchable queries grounded in the real world. |
| Outcome: | The proposed benchmark avoids web-searchable queries grounded in the real world and enables on-demand generation of new samples, mitigating the risk of data leakage. |