ELI5: Long Form Question Answering

Attention Is All You Need

Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018) · arXiv · doi:10.48550/arxiv.1810.04805 · Cited by 73,173

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1423 · Cited by 54,325

Untitled work

(2004) · Cited by 9,718

Enriching Word Vectors with Subword Information

Bojanowski, P., Grave, E., Joulin, A., & Mikolov, T. (2017) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00051 · Cited by 9,645

SQuAD: 100,000+ Questions for Machine Comprehension of Text

Rajpurkar, P., Zhang, J., Lopyrev, K., & Liang, P. (2016) · Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d16-1264 · Cited by 6,208

Billion-scale similarity search with GPUs

Johnson, J., Douze, M., & Jegou, H. (2021) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2019.2921572 · Cited by 4,228

Convolutional Sequence to Sequence Learning

Gehring, J., Auli, M., Grangier, D., Yarats, D., & Dauphin, Y. N. (2017) · arXiv · doi:10.48550/arxiv.1705.03122 · Cited by 2,850 · cited 2×

MS MARCO: A Human Generated MAchine Reading COmprehension Dataset

Bajaj, P., Campos, D., Craswell, N., Deng, L., Gao, J., Liu, X., Majumder, R., McNamara, A., Mitra, B., Nguyen, T., et al. (2016) · arXiv · doi:10.48550/arxiv.1611.09268 · Cited by 2,473

Know What You Don’t Know: Unanswerable Questions for SQuAD

Rajpurkar, P., Jia, R., & Liang, P. (2018) · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) · doi:10.18653/v1/p18-2124 · Cited by 2,240

Artificial intelligence content detection techniques using watermarking: A survey

Kumar, N., & Singh, A. K. (2025) · Image and Vision Computing · doi:10.1016/j.imavis.2025.105728 · Cited by 4

Modeling and Analysis of Question Answering for Long Context and Compound Questions Using BERT Model

Sivakumar, S., & Meenakshi, S. P. (2025) · Procedia Computer Science · doi:10.1016/j.procs.2025.04.529 · Cited by 4

How About Kind of Generating Hedges using End-to-End Neural Models?

Abulimiti, A., Clavel, C., & Cassell, J. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.50 · Cited by 7

ELQA: A Corpus of Metalinguistic Questions and Answers about English

Behzad, S., Sakaguchi, K., Schneider, N., & Zeldes, A. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.113 · Cited by 9

Bringing the State-of-the-Art to Customers: A Neural Agent Assistant Framework for Customer Service Support

Obadinma, S., Khattak, F. K., Wang, S. V., Sidhom, T., Lau, E., Robertson, S., Niu, J., Au, W., Munim, A., Bhaskar, K. R. K., et al. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-industry.44 · Cited by 5

Beyond Relevant Documents: A Knowledge-Intensive Approach for Query-Focused Summarization Using Large Language Models

Zhang, W., Huang, J.-H., Vakulenko, S., Xu, Y., Rajapakse, T. C., & Kanoulas, E. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-78495-8_6 · Cited by 8

On the Robustness of Generative Information Retrieval Models: An Out-of-Distribution Perspective

Liu, Y.-A., Zhang, R., Guo, J., Zhou, C., de Rijke, M., & Cheng, X. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-88711-6_26 · Cited by 12

PrimeQA: The Prime Repository for State-of-the-Art Multilingual Question Answering Research and Development

Sil, A., Sen, J., Iyer, B., Franz, M., Fadnis, K., Bornea, M. A., Rosenthal, S., McCarley, S., Zhang, R., Kumar, V., et al. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-demo.5 · Cited by 8

Concise Answers to Complex Questions: Summarization of Long-form Answers

Potluri, A., Xu, F., & Choi, E. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.541 · Cited by 7

Generating Fact Checking BriefsPreprint

Fan, A., Piktus, A., Petroni, F., Wenzek, G., Saeidi, M., Vlachos, A., Bordes, A., & Riedel, S. (2020) · arXiv (Cornell University) · doi:10.17863/cam.78760 · Cited by 4

Enhancing LLM’s Reliability by Iterative Verification Attributions with Keyword Fronting

Sui, Y., Ren, J., Tan, H., Chen, H., Li, Z., & Wang, J. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-70365-2_15 · Cited by 3

Researchy Questions: A Dataset of Multi-Perspective, Decompositional Questions for Deep Research

Rosset, C., Chung, H.-L., Qin, G., Chau, E. C., Feng, Z., Awadallah, A. H., Neville, J., & Rao, N. (2025) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3726302.3730275 · Cited by 49

An Empirical Study of Evaluating Long-form Question Answering

Xian, N., Fan, Y., Zhang, R., de Rijke, M., & Guo, J. (2025) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3726302.3729895 · Cited by 5

Examining Patients Length of Stay Estimation with Explainable Artificial Intelligence Methods

Arslanoğlu, K., & Karaköse, M. (2024) · Information systems engineering and management · doi:10.1007/978-3-031-71125-1_25 · Cited by 3

EvidenceMap: Learning evidence analysis to unleash the power of small language models for biomedical question answering

Zong, C. F., Wan, J., Tang, S., & Zhang, L. (2025) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2025.103246 · Cited by 4

ChiQA

Wang, B., Lv, F., Yao, T., Ma, J., Luo, Y. B., & Liang, H. (2022) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3511808.3557258 · Cited by 3

Feature Importance in Explainable AI for Expounding Black Box Models

Bhuyan, B. P., & Srivastava, S. (2023) · Lecture notes in networks and systems · doi:10.1007/978-981-19-6634-7_58 · Cited by 3

How Natural Language Processing Enables AIGC Recognition? --Latest Trends and Future Prospects

Wang, W., & Qiao, H. (2024) · International Conference on Software Engineering and Information Management (ICSIM) · doi:10.1145/3647722.3647738 · Cited by 3

Descriptor: Open-Domain Long-Form Context-Aware Question-Answering Dataset (DragonVerseQA)

Lahiri, A. K., & Hu, Q. (2025) · IEEE data descriptions. · doi:10.1109/ieeedata.2025.3562173 · Cited by 3

Modeling Extractive Question Answering Using Encoder-Decoder Models with Constrained Decoding and Evaluation-Based Reinforcement Learning

Li, S., Sun, C., Liu, B., Liu, Y., & Ji, Z. (2023) · Mathematics · doi:10.3390/math11071624 · Cited by 3

M3: A Multi-View Fusion and Multi-Decoding Network for Multi-Document Reading Comprehension

Wen, L., Wang, H., Luo, Y., & Wang, X. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.94 · Cited by 3

Limits and Challenges of Embedding-based Question Answering in Export Control Expert System

Rzepka, R., Shirafuji, D., & Obayashi, A. (2021) · Procedia Computer Science · doi:10.1016/j.procs.2021.09.041 · Cited by 3

Extractive-Boolean Question Answering for Scientific Fact Checking

Rakotoson, L., Letaillieur, C., Massip, S., & Laleye, F. A. A. (2022) · doi:10.1145/3512732.3533580 · Cited by 3

Using Local Knowledge Graph Construction to Scale Seq2Seq Models to Multi-Document InputsPreprint

Fan, A., Gardent, C., Braud, C., & Bordes, A. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1910.08435 · Cited by 16

Multi-Perspective Abstractive Answer SummarizationPreprint

Fabbri, A. R., Wu, X., Iyer, S. V., & Diab, M. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2104.08536 · Cited by 9

BrowseComp: A Simple Yet Challenging Benchmark for Browsing Agents

Wei, J., Sun, Z., Papay, S., McKinney, S., Han, J., Fulford, I., Chung, H. W., Passos, A. T., Fedus, W., & Glaese, A. (2025) · arXiv · doi:10.48550/arxiv.2504.12516 · Cited by 2

On the Capacity of Citation Generation by Large Language Models

Qian, H., Fan, Y., Zhang, R., & Guo, J. (2025) · Lecture notes in computer science · doi:10.1007/978-981-96-1710-4_9 · Cited by 6

Boosting Retrieval-Augmented Generation with Generation-Augmented Retrieval: A Co-Training Approach

Tang, Y., Zhang, R., Guo, J., de Rijke, M., Fan, Y., & Cheng, X. (2025) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3726302.3729907 · Cited by 3

WebFAQ: A Multilingual Collection of Natural Q&A Datasets for Dense Retrieval

Dinzinger, M., Caspari, L., Dastidar, K. G., Mitrović, J., & Granitzer, M. (2025) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3726302.3731934 · Cited by 8

Towards Reliable and Transparent Diabetes Diagnosis with an Explainable Ensemble Framework for Clinical Decision Support

Khokhar, P. B., Pentangelo, V., Palomba, F., & Gravino, C. (2025) · International Conference on Artificial Intelligence, Computer, Data Sciences and Applications (ACDSA) · doi:10.1109/acdsa65407.2025.11166460 · Cited by 2

Learning to rank under uncertainty: a robust neural approach

Khodabakhsh, M., & Bagheri, E. (2025) · Knowledge and Information Systems · doi:10.1007/s10115-025-02601-4 · Cited by 2

Synergizing linguistic features and transformer networks for detecting AI-generated text

Yadagiri, A., Kumar, P., Poddar, Y., Pakray, P., & Chunka, C. (2026) · Knowledge and Information Systems · doi:10.1007/s10115-025-02637-6 · Cited by 2

Down and Across: Introducing Crossword-Solving as a New NLP Benchmark

Kulshreshtha, S., Kovaleva, O., Shivagunde, N., & Rumshisky, A. (2022) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2022.acl-long.189 · Cited by 4

Open-Domain Long-Form Question–Answering Using Transformer-Based Pipeline

Dash, A., Awachar, M., Patel, A. M., & Rudra, B. (2023) · SN Computer Science · doi:10.1007/s42979-023-02039-x · Cited by 3

Axiomatic Preference Modeling for Longform Question Answering

Rosset, C., Zheng, G., Dibia, V. C., Awadallah, A. H., & Bennett, P. N. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.702 · Cited by 2

Efficient Machine Reading Comprehension for Health Care Applications: Algorithm Development and Validation of a Context Extraction Approach

Nguyen, D.-A., Li, M., Lambert, G. W., Kowalczyk, R., McDonald, R. L., & Vo, Q. B. (2024) · JMIR Formative Research · doi:10.2196/52482 · Cited by 2

Evaluation of Medium-Sized Language Models in German and English Language

Peinl, R., & Wirth, J. (2024) · International Journal on Natural Language Computing · doi:10.5121/ijnlc.2024.13101 · Cited by 2

E2E-AFG: An End-to-End Model with Adaptive Filtering for Retrieval-Augmented Generation

Jiang, Y., Xie, Z., Zhang, W., Fang, Y., & Pan, S. (2025) · Lecture notes in computer science · doi:10.1007/978-981-96-8197-6_8 · Cited by 2

CorpusBrain++: A Continual Generative Pre-Training Framework for Knowledge-Intensive Language Tasks

Guo, J., Zhou, C., Zhang, R., Chen, J., de Rijke, M., Fan, Y., & Cheng, X. (2025) · ACM Transactions on Information Systems · doi:10.1145/3763233 · Cited by 10

TMT: Tri-Modal Translation Between Speech, Image, and Text by Processing Different Modalities as Different Languages

Kim, M., Jung, J.-W., Rha, H., Maiti, S., Arora, S., Chang, X., Watanabe, S., & Ro, Y. M. (2026) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2026.3659297 · Cited by 9

iLFQA: A Platform for Efficient and Accurate Long-Form Question Answering

Butler, R. S., Duggirala, V. D., & Banaei‐Kashani, F. (2022) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/3488560.3502190 · Cited by 3

Generating Coherent Narratives with Subtopic Planning to Answer How-to Questions

Cai, P., Yu, M., Liu, F., & Yu, H. (2022) · Workshop on Generation, Evaluation and Metrics (GEM) · doi:10.18653/v1/2022.gem-1.3 · Cited by 3

Neural Ranking with Weak Supervision for Open-Domain Question Answering : A Survey

Shen, X., Vakulenko, S., Tredici, M. D., Barlacchi, G., Byrne, B., & de Gispert, A. (2023) · Findings of the Association for Computational Linguistics: EACL · doi:10.18653/v1/2023.findings-eacl.129 · Cited by 2

Don’t Add, don’t Miss: Effective Content Preserving Generation from Pre-Selected Text Spans

Slobodkin, A., Caciularu, A., Hirsch, E., & Dagan, I. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.852 · Cited by 3

Multi-Document Answer Generation for Non-Factoid Questions

Baranova-Bolotova, V. (2020) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3397271.3401449 · Cited by 2

Efficient Prediction of Cardiovascular Disease by Fusing Boosting Classifiers Combined with Explainable Approach

Rahman, N., Jahan, F., & Alam, F. I. (2022) · International Conference on Innovations in Science, Engineering and Technology (ICISET) · doi:10.1109/iciset54810.2022.9775826 · Cited by 2

Graph-enhanced multi-answer summarization under question-driven guidance

Li, B., Yang, P., Hu, Z., Sun, Y., & Yi, M. (2023) · The Journal of Supercomputing · doi:10.1007/s11227-023-05457-z · Cited by 2

Parameter-efficient Weight Ensembling Facilitates Task-level Knowledge Transfer

Lv, X., Ding, N., Qin, Y., Liu, Z., & Sun, M. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-short.24 · Cited by 3

MoQA: Benchmarking Multi-Type Open-Domain Question Answering

Yen, H., Gao, T., Lee, J., & Chen, D. (2023) · DialDoc Workshop on Document-grounded Dialogue and Conversational Question Answering (DialDoc) · doi:10.18653/v1/2023.dialdoc-1.2 · Cited by 2

teamPN at SemEval-2023 Task 1: Visual Word Sense Disambiguation Using Zero-Shot MultiModal Approach

Katyal, N., Rajpoot, P. K., Tamilarasu, S., & Mustafi, J. (2023) · International Workshop on Semantic Evaluation (SemEval) · doi:10.18653/v1/2023.semeval-1.63 · Cited by 2

PreWoMe: Exploiting Presuppositions as Working Memory for Long Form Question Answering

Han, W., Park, J., & Lee, K. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.517 · Cited by 2

Natural Language Generation

Albert Gatt, A. (2025) · Oxford University Press eBooks · doi:10.1093/acrefore/9780199384655.013.896 · Cited by 2

DocTabQA: Answering Questions from Long Documents Using Tables

Wang, H., Hu, K., Dong, H., & Gao, L. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-70533-5_27 · Cited by 2

Retrieval-Augmented Multilingual Citation Generation

Liang, X., Niu, S., Zhang, S., Li, Z., Zhang, X., Wu, B., Xiong, F., Tang, B., Wang, H., Song, S., et al. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10888712 · Cited by 1

Design of web application for community answering using long short term memory compared over hidden markov model with improved accuracy

Reddy, A., & Arumugam, S. S. (2025) · AIP conference proceedings · doi:10.1063/5.0262661 · Cited by 1

PickLLM: Context-Aware RL-Assisted Large Language Model Routing

Sikeridis, D., Ramdass, D., & Pareek, P. (2025) · Communications in computer and information science · doi:10.1007/978-981-96-8912-5_11 · Cited by 11

Learning Refined Document Representations for Dense Retrieval via Deliberate Thinking

Ji, Y., Xu, Z., Liu, Z., Yan, Y., Yu, S., Li, Y., Liu, Z., Gu, Y., Yu, G., & Sun, M. (2025) · Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region (SIGIR-AP) · doi:10.1145/3767695.3769491 · Cited by 19

Learning to ask and answer in specialized documents: Exemplifying through modular integrated construction regulatory documents

Wei, Y., Li, X., Huang, Z., Lin, Z., & Zhang, N. (2026) · Computers in Industry · doi:10.1016/j.compind.2026.104462 · Cited by 1

ExpandFuse: A Hybrid Retrieval Framework with Query Expansion and Topic-Aware Reranking for Multi-Hop Question Answering

Murugaraj, K., Lamsiyah, S., & Theobald, M. (2025) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata66926.2025.11402490 · Cited by 1

PlanEval: Augmenting Large Language Models With a Small-Scale Planner-Evaluator for Knowledge-Intensive Tasks

Li, W., Wang, J., Yu, L.-C., & Zhang, X. (2026) · IEEE Transactions on Audio Speech and Language Processing · doi:10.1109/taslpro.2026.3677587 · Cited by 1

Delayed Backdoor: Let the Trigger Fly for a While in Backdoor Attack on Internet of Things

Liu, B., Zhu, P., Zhao, S., Chen, X., Huang, H., Shi, L., Wang, X., Zheng, Z., & Yang, L. T. (2026) · IEEE Internet of Things Journal · doi:10.1109/jiot.2026.3686078 · Cited by 1

Separating Wheat from Chaff: Fine-Grained Defenses Against Poisoning in Multi-Perspective RAG

Zhao, Y., He, L., Sun, L., & Su, S. (2026) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3805712.3809895 · Cited by 1

GANDALF: a General Character Name Description Dataset for Long Fiction

Carlsson, F., Sahlgren, M., Olsson, F., & Gyllensten, A. C. (2021) · Workshop on Machine Reading for Question Answering (MRQA) · doi:10.18653/v1/2021.mrqa-1.13 · Cited by 1

Question Answering

Aggarwal, C. C. (2022) · Springer eBooks · doi:10.1007/978-3-030-96623-2_14 · Cited by 1

DynamicTOC: Persona-based Table of Contents for Consumption of Long Documents

Maheshwari, H., Sivakumar, N., Jain, S., Karandikar, T., Aggarwal, V., Goyal, N., & Shekhar, S. (2022) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2022.naacl-main.378 · Cited by 1

TWIZ

Ferreira, R., Silva, D., Tavares, D., Vicente, F., Bonito, M., Gonçalves, G., Margarido, R., Figueiredo, P., Rodrigues, H., Semedo, D., et al. (2022) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3503161.3547741 · Cited by 1

Foundation Models for Text Generation

Paaß, G., & Giesselbach, S. (2023) · Artificial intelligence: foundations, theory, and algorithms/Artificial intelligence: Foundations, theory, and algorithms · doi:10.1007/978-3-031-23190-2_6 · Cited by 3

Deep QA: An Open-Domain Dataset of Deep Questions and Comprehensive Answers

Anbarasu, H. S., Navalli, H. V., Vidapanakal, H., Gowd, K. M., & Das, B. (2023) · Communications in computer and information science · doi:10.1007/978-3-031-35299-7_16 · Cited by 1

An Interpretable Hybrid Recommender Based on Graph Convolution to Address Serendipity

Uppal, A., Maitreyi, P., Shreya, P., Jain, T., & Das, B. (2023) · Communications in computer and information science · doi:10.1007/978-3-031-35299-7_3 · Cited by 1

Revealing the Blind Spot of Sentence Encoder Evaluation by HEROS

Chiang, C.-H., Lee, H.-Y., Chuang, Y.-S., & Glass, J. R. (2023) · Workshop on Representation Learning for NLP (RepL4NLP) · doi:10.18653/v1/2023.repl4nlp-1.24 · Cited by 2

Different Tunes Played with Equal Skill: Exploring a Unified Optimization Subspace for Parameter-Efficient Tuning

Yi, J., Chen, W., Qin, Y., Lin, Y., Ding, N., Xu, H., Liu, Z., Sun, M., & Zhou, J. (2022) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2022.findings-emnlp.244 · Cited by 1

Reinforcement Replaces Supervision: Query focused Summarization using Deep Reinforcement Learning

Nath, S., Bhattacharyya, P., & Khadilkar, H. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.977 · Cited by 1

Ensemble of explainable artificial intelligence predictions through discriminate regions: A model to identify COVID-19 from chest X-ray images

Koyyada, S. P., & Singh, T. P. (2023) · Journal of Intelligent Systems · doi:10.1515/jisys-2023-0163 · Cited by 2

Towards Better Statistical Understanding of Watermarking LLMs

Cai, Z., Liu, S., Wang, H., Zhong, H., & Li, X. (2026) · Journal of the American Statistical Association · doi:10.1080/01621459.2026.2618290 · Cited by 17

Knowledge-augmented Methods for Natural Language Understanding

Jiang, M., Lin, B. Y., Wang, S., Xu, Y., Yu, W., & Zhu, C. (2024) · SpringerBriefs in computer science · doi:10.1007/978-981-97-0747-8_3 · Cited by 1

Data Augmentation with In-Context Learning and Comparative Evaluation in Math Word Problem Solving

Yiğit, G., & Amasyalı, M. F. (2024) · SN Computer Science · doi:10.1007/s42979-024-02853-x · Cited by 2

Venn Diagram Prompting: Accelerating Comprehension with Scaffolding Effect

Mahendru, S., & Pandit, T. (2024) · World Symposium on Artificial Intelligence (WSAI) · doi:10.1109/wsai62426.2024.10828919 · Cited by 1

NeST: The neuro-symbolic transpiler

Pfanschilling, V., Shindo, H., Dhami, D. S., & Kersting, K. (2025) · International Journal of Approximate Reasoning · doi:10.1016/j.ijar.2025.109369 · Cited by 3

Ranking Generated Answers

Heineking, S., Probst, J., Steinbach, D., Potthast, M., & Scells, H. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-88714-7_10 · Cited by 1

HouseOfTheDragonQA: Open-Domain Long-form Context-Aware QA Pairs for TV Series

Lahiri, A. K., & Hu, Q. (2024) · IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT) · doi:10.1109/wi-iat62293.2024.00028 · Cited by 1

Generating Response-Specific Elaborated Feedback Using Long-Form Neural Question Answering

Olney, A. M. (2021) · ACM Conference on Learning @ Scale (L@S) · doi:10.1145/3430895.3460131 · Cited by 1

A Framework for Evaluating MRC Approaches with Unanswerable Questions

Du, H., Thudumu, S., Singh, S., Barnett, S., Logothetis, I., Vasa, R., & Mouzakis, K. (2022) · IEEE International Conference on eScience · doi:10.1109/escience55777.2022.00075 · Cited by 1

Gestalt: a Stacking Ensemble for SQuAD2.0Preprint

El-Geish, M. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2004.07067 · Cited by 2

Which Linguist Invented the Lightbulb? Presupposition Verification for Question-AnsweringPreprint

Kim, N., Pavlick, E., Ayan, B. K., & Ramachandran, D. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2101.00391 · Cited by 1

D2S: Document-to-Slide Generation Via Query-Based Text SummarizationPreprint

Sun, E., Hou, Y., Wang, D., Zhang, Y., & Wang, N. X. R. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2105.03664 · Cited by 1

Hallucinations in generative artificial intelligence and large language models: tests, datasets, detection and correction methods

Naser, M. Z. (2026) · Language Resources and Evaluation · doi:10.1007/s10579-026-09938-4 · Cited by 2

R4C: A Benchmark for Evaluating RC Systems to Get the Right Answer for the Right ReasonPreprint

Inoue, N., Stenetorp, P., & Inui, K. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1910.04601

Aspect-Oriented Summarization through Query-Focused Extraction.Preprint

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