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

Effect of Explainable Artificial Intelligence on Trust of Mental Health Professionals in an AI-Based System for Suicide Prevention

de Oliveira, A. C., Azevedo, J. P. C., Ruback, L., Moreira, R., Teixeira, S. S., & Teles, A. S. (2025) · IEEE Access · doi:10.1109/access.2025.3556245 · Cited by 27

Shall We Pretrain Autoregressive Language Models with Retrieval? A Comprehensive Study

Wang, B., Ping, W., Xu, P., McAfee, L., Liu, Z., Shoeybi, M., Dong, Y., Kuchaiev, O., Li, B., Xiao, C., et al. (2023) · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2023.emnlp-main.482 · Cited by 21

Explainable Feature Learning for Predicting Neonatal Intensive Care Unit (NICU) Admissions

Marvin, G., & Alam, M. G. R. (2021) · IEEE International Conference on Biomedical Engineering, Computer and Information Technology for Health (BECITHCON) · doi:10.1109/becithcon54710.2021.9893719 · Cited by 20

A Primer on Seq2Seq Models for Generative Chatbots

Scotti, V., Sbattella, L., & Tedesco, R. (2023) · ACM Computing Surveys · doi:10.1145/3604281 · Cited by 29

M4: Multi-generator, Multi-domain, and Multi-lingual Black-Box Machine-Generated Text Detection

Wang, Y., Mansurov, J., Ivanov, P., Su, J., Shelmanov, A., Tsvigun, A., Whitehouse, C., Afzal, O. M., Mahmoud, T., Sasaki, T., et al. (2023) · arXiv · doi:10.48550/arxiv.2305.14902 · Cited by 19

Multi-hop Inference for Question-driven Summarization

Deng, Y., Zhang, W., & Lam, W. P. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.547 · Cited by 19

Towards a Search Engine for Machines: Unified Ranking for Multiple Retrieval-Augmented Large Language Models

Salemi, A., & Zamani, H. (2024) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3626772.3657733 · Cited by 35

Untitled work

(2025) · doi:10.1145/3833415 · Cited by 18

Resilience Assessment of Large Language Models under Transient Hardware Faults

Agarwal, U. K., Chan, A., & Pattabiraman, K. (2023) · Proceedings/Proceedings - International Symposium on Software Reliability Engineering · doi:10.1109/issre59848.2023.00052 · Cited by 21

Explainable AI for Healthcare: A Study for Interpreting Diabetes Prediction

Gandhi, N., & Mishra, S. (2021) · Lecture notes in networks and systems · doi:10.1007/978-3-030-82469-3_9 · Cited by 18

Hierarchical Sliding Inference Generator for Question-driven Abstractive Answer Summarization

Li, B., Yang, P., Zhao, H., Zhang, P., & Liu, Z. (2022) · ACM Transactions on Information Systems · doi:10.1145/3511891 · Cited by 17

HtmlRAG: HTML is Better Than Plain Text for Modeling Retrieved Knowledge in RAG Systems

Tan, J., Dou, Z., Wang, W., Wang, M., Chen, W., & Wen, J.-R. (2025) · International World Wide Web Conference (WWW) · doi:10.1145/3696410.3714546 · Cited by 48

NLQuAD: A Non-Factoid Long Question Answering Data Set

Soleimani, A., Monz, C., & Worring, M. (2021) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/2021.eacl-main.106 · Cited by 17

Chain-of-Thought Improves Text Generation with Citations in Large Language Models

Ji, B., Liu, H., Du, M., & Ng, S.-K. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i16.29794 · Cited by 19

Rogue Scores

Grusky, M. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.107 · Cited by 16

Beat LLMs at their own game: Zero-shot LLM-generated text detection via querying ChatGPT

Zhu, B., Yuan, L., Cui, G., Chen, Y., Fu, C., He, B., Deng, Y., Liu, Z., Sun, M., & Gu, M. (2023) · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2023.emnlp-main.463 · Cited by 16

FoRAG: Factuality-optimized Retrieval Augmented Generation for Web-enhanced Long-form Question Answering

Cai, T., Tan, Z., Song, X., Sun, T., Jiang, J., Xu, Y., Zhang, Y., & Gu, J. (2024) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3637528.3672065 · Cited by 24

Toward Robust Arabic AI-Generated Text Detection: Tackling Diacritics Challenges

Alshammari, H., & Elleithy, K. M. (2024) · Information · doi:10.3390/info15070419 · Cited by 21

Evaluating Verifiability in Generative Search Engines

Liu, N. F., Zhang, T., & Liang, P. (2023) · arXiv · Cited by 143

Quantifying confidence shifts in a BERT-based question answering system evaluated on perturbed instances

Shen, K., & Kejriwal, M. (2023) · PLoS ONE · doi:10.1371/journal.pone.0295925 · Cited by 17

CREPE: Open-Domain Question Answering with False Presuppositions

Yu, X., Min, S., Zettlemoyer, L., & Hajishirzi, H. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.583 · Cited by 35

WikiHowQA: A Comprehensive Benchmark for Multi-Document Non-Factoid Question Answering

Bolotova-Baranova, V., Blinov, V., Филиппова, С. В., Scholer, F., & Sanderson, M. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.290 · Cited by 15

A Joint LLM-KG System for Disease Q&A

Sukhwal, P. C., Rajan, V., & Kankanhalli, A. (2024) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2024.3514659 · Cited by 15

CLAPnq : Cohesive Long-form Answers from Passages in Natural Questions for RAG systems

Rosenthal, S., Sil, A., Florian, R., & Roukos, S. (2025) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00729 · Cited by 29

Navigating challenges/opportunities in developing smart agricultural extension platforms: Multi-media data mining techniques

Kpodo, J., & Nejadhashemi, A. P. (2025) · Artificial Intelligence in Agriculture · doi:10.1016/j.aiia.2025.04.001 · Cited by 19

Bridging Hierarchical and Sequential Context Modeling for Question-driven Extractive Answer Summarization

Deng, Y., Zhang, W., Li, Y., Yang, M., Lam, W. P., & Shen, Y. (2020) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3397271.3401208 · Cited by 14

KPQA: A Metric for Generative Question Answering Using Keyphrase Weights

Lee, H., Yoon, S., Dernoncourt, F., Kim, D. S., Bui, T., Shin, J., & Jung, K. (2021) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2021.naacl-main.170 · Cited by 18

CCQA: A New Web-Scale Question Answering Dataset for Model Pre-Training

Huber, P., Aghajanyan, A., Oğuz, B., Okhonko, D., Yih, S., Gupta, S., & Chen, X. (2022) · Findings of the Association for Computational Linguistics: NAACL · doi:10.18653/v1/2022.findings-naacl.184 · Cited by 16

Fourier Transformer: Fast Long Range Modeling by Removing Sequence Redundancy with FFT Operator

He, Z., Yang, M., Feng, M., Yin, J., Wang, X., Leng, J., & Lin, Z. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.570 · Cited by 26

Generate, Discriminate and Contrast: A Semi-Supervised Sentence Representation Learning Framework

Chen, Y., Zhang, Y., Wang, B., Liu, Z., & Li, H. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.558 · Cited by 13

Leveraging Structured Information for Explainable Multi-hop Question Answering and Reasoning

Li, R., & Du, X. (2023) · Findings of the Association for Computational Linguistics: EMNLP 2023 · doi:10.18653/v1/2023.findings-emnlp.452 · Cited by 12

Ragnarök: A Reusable RAG Framework and Baselines for TREC 2024 Retrieval-Augmented Generation Track

Pradeep, R., Thakur, N., Sharifymoghaddam, S., Zhang, E., Nguyen, R., Campos, D., Craswell, N., & Lin, J. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-88708-6_9 · Cited by 46

Bidirectional Encoder Representation from Transformers (BERT) Variants for Procedural Long-Form Answer Extraction

Nitish, S., Darsini, R., Shashank, G. S., Tejas, V. R., & Arya, A. (2022) · International Conference on Cloud Computing, Data Science & Engineering (Confluence) · doi:10.1109/confluence52989.2022.9734142 · Cited by 12

From text to multimodal: a survey of adversarial example generation in question answering systems

Yiğit, G., & Amasyalı, M. F. (2024) · Knowledge and Information Systems · doi:10.1007/s10115-024-02199-z · Cited by 13

Toward Robust Evaluation: A Comprehensive Taxonomy of Datasets and Metrics for Open Domain Question Answering in the Era of Large Language Models

Srivastava, A., & Memon, A. M. (2024) · IEEE Access · doi:10.1109/access.2024.3446854 · Cited by 14

Context Embeddings for Efficient Answer Generation in Retrieval-Augmented Generation

Rau, D., Wang, S., Déjean, H., Clinchant, S., & Kamps, J. (2025) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/3701551.3703527 · Cited by 11

Performance Aware LLM Load Balancer for Mixed Workloads

Jain, K., Parayil, A., Mallick, A., Choukse, E., Qin, X., Zhang, J., Goiri, Í., Wang, R., Bansal, C., Rühle, V., et al. (2025) · Workshop on Machine Learning and Systems (EuroMLSys) · doi:10.1145/3721146.3721947 · Cited by 11

mt RAG: A Multi-Turn Conversational Benchmark for Evaluating Retrieval-Augmented Generation Systems

Katsis, Y., Rosenthal, S., Fadnis, K., Gunasekara, C., Lee, Y., Popa, L., Shah, V., Zhu, H., Contractor, D., & Danilevsky, M. (2025) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl.a.19 · Cited by 60

How Do We Answer Complex Questions: Discourse Structure of Long-form Answers

Xu, F., Li, J. J., & Choi, E. (2022) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2022.acl-long.249 · Cited by 18

Learning to Initialize: Can Meta Learning Improve Cross-task Generalization in Prompt Tuning?

Qin, C., Joty, S., Li, Q., & Zhao, R. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.659 · Cited by 14

Closed-book Question Generation via Contrastive Learning

Dong, X., Lu, J., Wang, J., & Caverlee, J. (2023) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/2023.eacl-main.230 · Cited by 11

TLDR: Token Loss Dynamic Reweighting for Reducing Repetitive Utterance GenerationPreprint

Jiang, S., Wolf, T., Monz, C., & de Rijke, M. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.11963 · Cited by 16

Attention-guided Generative Models for Extractive Question AnsweringPreprint

Xu, P., Liang, D., Huang, Z., & Xiang, B. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.06393 · Cited by 22

Universality of representation in biological and artificial neural networks

Hosseini, E., Casto, C., Zaslavsky, N., Conwell, C., Richardson, M., & Fedorenko, E. (2024) · doi:10.1101/2024.12.26.629294 · Cited by 10

MAGE: Machine-generated Text Detection in the Wild

Li, Y., Li, Q., Cui, L., Bi, W., Wang, Z., Wang, L., Yang, L., Shi, S., & Zhang, Y. (2023) · arXiv · doi:10.48550/arxiv.2305.13242 · Cited by 10

Long Document Classification in the Transformer Era: A Survey on Challenges, Advances, and Open Issues

Principe, R. A. A., Chiarini, N., & Viviani, M. (2025) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.70019 · Cited by 16

Generating Biographies on Wikipedia: The Impact of Gender Bias on the Retrieval-Based Generation of Women Biographies

Fan, A., & Gardent, C. (2022) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2022.acl-long.586 · Cited by 21

How can machine learning identify suicidal ideation from user's texts? Towards the explanation of the Boamente system

de Oliveira, A. C., Diniz, E. J. D. S., Teixeira, S. S., & Teles, A. S. (2022) · Procedia Computer Science · doi:10.1016/j.procs.2022.09.093 · Cited by 10

More Than Reading Comprehension: A Survey on Datasets and Metrics of Textual Question AnsweringPreprint

Bai, Y., & Wang, D. Z. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2109.12264 · Cited by 19

Discourse Comprehension: A Question Answering Framework to Represent Sentence Connections

Ko, W.-J., Dalton, C., Simmons, M., Fisher, E., Durrett, G., & Li, J. J. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.806 · Cited by 10

A Unified Encoder-Decoder Framework with Entity Memory

Zhang, Z., Yu, W., Zhu, C., & Jiang, M. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.43 · Cited by 13

Pt-HotpotQA: Evaluating Multi-Hop Question Answering on Original and Portuguese-translated Datasets Using LLMs

Mucciaccia, S. S., Paixão, T. M., Mutz, F. W., Souza, A. F. D., Badue, C. S., & Oliveira-Santos, T. (2025) · Journal of the Brazilian Computer Society · doi:10.5753/jbcs.2025.5801 · Cited by 10

A Study of Automatic Metrics for the Evaluation of Natural Language Explanations

Clinciu, M., Eshghi, A., & Hastie, H. (2021) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/2021.eacl-main.202 · Cited by 13

Generation-Focused Table-Based Intermediate Pre-training for Free-Form Question Answering

Shi, P. J., Ng, P., Nan, F., Zhu, H., Wang, J., Jiang, J., Li, A. H., Chakravarti, R., Weidner, D., Xiang, B., et al. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i10.21382 · Cited by 11

MTRAG: A Multi-Turn Conversational Benchmark for Evaluating Retrieval-Augmented Generation SystemsPreprint

Katsis, Y., Rosenthal, S., Fadnis, K., Gunasekara, C., Lee, Y., Popa, L., Shah, V., Zhu, H., Contractor, D., & Danilevsky, M. (2025) · Qeios · doi:10.32388/phs6yn · Cited by 9

Evaluation Paradigms in Question Answering

Rodríguez, P., & Boyd-Graber, J. L. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.758 · Cited by 12

Exploring happiness factors with explainable ensemble learning in a global pandemic

Hamja, M. A., Hasan, M., Rashid, M. A., & Shourov, M. T. H. (2025) · PLoS ONE · doi:10.1371/journal.pone.0313276 · Cited by 9

Eliciting and Understanding Cross-task Skills with Task-level Mixture-of-Experts

Ye, Q., Zha, J., & Ren, X. (2022) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2022.findings-emnlp.189 · Cited by 19

GripRank: Bridging the Gap between Retrieval and Generation via the Generative Knowledge Improved Passage Ranking

Bai, J., Guo, H., Liu, J., Yang, J., Liang, X., Yan, Z., & Li, Z. (2023) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3583780.3614901 · Cited by 9

Improving Conditioning in Context-Aware Sequence to Sequence ModelsPreprint

Wang, X., Weston, J., Auli, M., & Jernite, Y. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1911.09728 · Cited by 9

Time and the Value of DataPreprint

Valavi, E., Hestness, J., Ardalani, N., & Iansiti, M. (2020) · SSRN Electronic Journal · doi:10.2139/ssrn.3680910 · Cited by 8

Datasets: A Community Library for Natural Language ProcessingPreprint

Lhoest, Q., del Moral, A. V., Jernite, Y., Thakur, A., von Platen, P., Patil, S. S., Chaumond, J., Drame, M., Plu, J., Tunstall, L., et al. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2109.02846 · Cited by 133

Explainable Machine Learning Model to Accurately Predict Protein-Binding Peptides

Azim, S. M., Balasubramanyam, A., Islam, S. R., Fu, J., & Dehzangi, A. (2024) · Algorithms · doi:10.3390/a17090409 · Cited by 7

Explainable AI in Smart Grid Applications

Kül, S., Arslan, B., & Sağıroğlu, Ş. (2025) · IEEE International Conference on Compatibility, Power Electronics and Power Engineering · doi:10.1109/cpe-powereng63314.2025.11027305 · Cited by 7

Enhancing legal assistance with AI: a comprehensive approach to intent classification and domain specific model tuning

Bhatnagar, M., & Huchhanavar, S. (2025) · Artificial Intelligence and Law · doi:10.1007/s10506-025-09441-1 · Cited by 7

AnswerSumm: A Manually-Curated Dataset and Pipeline for Answer Summarization

Fabbri, A. R., Wu, X., Iyer, S. V., Li, H., & Diab, M. (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.180 · Cited by 7

Outcome Prediction for SARS-CoV-2 Patients Using Machine Learning Modeling of Clinical, Radiological, and Radiomic Features Derived from Chest CT Images

Spagnoli, L., Morrone, M. F., Giampieri, E., Paolani, G., Santoro, M., Curti, N., Coppola, F., Ciccarese, F., Vara, G., Brandi, N., et al. (2022) · Applied Sciences · doi:10.3390/app12094493 · Cited by 7

Modeling Exemplification in Long-form Question Answering via Retrieval

Wang, S., Xu, F., Thompson, L., Choi, E., & Iyyer, M. (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.151 · Cited by 8

Visualizing and Explaining Language Models

Brașoveanu, A., & Andonie, R. (2022) · Studies in computational intelligence · doi:10.1007/978-3-030-93119-3_8 · Cited by 8

RELiC: Retrieving Evidence for Literary Claims

Thai, K., Chang, Y., Krishna, K., & Iyyer, M. (2022) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2022.acl-long.517 · Cited by 15

Evaluation of Medium-large Language Models at Zero-shot Closed Book Generative Question Answering

Peinl, R., & Wirth, J. (2024) · doi:10.5121/csit.2024.140106 · Cited by 8

FreshLLMs: Refreshing Large Language Models with Search Engine Augmentation

Vu, T., Iyyer, M., Wang, X., Constant, N., Wei, J., Wei, J., Tar, C., Sung, Y.-H., Zhou, D., Le, Q., et al. (2023) · arXiv · Cited by 7

Learning to generate and evaluate fact-checking explanations with transformers

Feher, D., Khered, A., Zhang, H., Batista-Navarro, R., & Schlegel, V. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.109492 · Cited by 11

RAG Powered LLMs for QA: Evolution, Challenges, Applications, and Future Directions

Zeeshan, H. M. A., Faizan, M., Zia, U., & Gohar, A. (2025) · International Conference on Communication Technologies (ComTech) · doi:10.1109/comtech65062.2025.11034531 · Cited by 6

Debiasing Watermarks for Large Language Models via Maximal Coupling

Xie, Y., Li, X., Mallick, T., Su, W., & Zhang, R. (2025) · Journal of the American Statistical Association · doi:10.1080/01621459.2025.2520455 · Cited by 21

Optimizing Retrieval-augmented Reader Models via Token Elimination

Berchansky, M., Izsak, P., Caciularu, A., Dagan, I., & Wasserblat, M. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.93 · Cited by 16

Language Models for Predicting Organic Synthesis Procedures

Vaškevičius, M., & Kapočiūtė-Dzikienė, J. (2024) · Applied Sciences · doi:10.3390/app142411526 · Cited by 6

From data to information

Paley, A., Zhao, A. L. L., Pack, H., Servantez, S., Adler, R. F., Sterbentz, M., Pah, A. R., Schwartz, D. L., Barrie, C., Einarsson, A., et al. (2021) · International Conference on Artificial Intelligence and Law · doi:10.1145/3462757.3466100 · Cited by 7

ChapterBreak: A Challenge Dataset for Long-Range Language Models

Sun, S., Thai, K., & Iyyer, M. (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.271 · Cited by 13

TABi: Type-Aware Bi-Encoders for Open-Domain Entity Retrieval

Leszczynski, M., Fu, D. Y., Chen, M. F., & Ré, C. (2022) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2022.findings-acl.169 · Cited by 9

How Train–Test Leakage Affects Zero-Shot Retrieval

Fröbe, M., Akiki, C., Potthast, M., & Hagen, M. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-20643-6_11 · Cited by 6

CONSISTENT: Open-Ended Question Generation From News Articles

Chakrabarty, T., Lewis, J., & Muresan, S. (2022) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2022.findings-emnlp.517 · Cited by 6

Query Refinement Prompts for Closed-Book Long-Form QA

Amplayo, R. K., Webster, K., Collins, M., Das, D., & Narayan, S. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.444 · Cited by 23

Semantic Verification in Large Language Model-based Retrieval Augmented Generation

Martin, A., Witschel, H. F., Mandl, M. M., & Stockhecke, M. (2024) · Proceedings of the AAAI Symposium Series · doi:10.1609/aaaiss.v3i1.31199 · Cited by 7

Efficient Tuning of Large Language Models for Knowledge-Grounded Dialogue Generation

Zhang, B., Ma, H., Li, D., Ding, J., Wang, J., Xu, B., & Lin, H. (2025) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl.a.17 · Cited by 5

Techniques, datasets, evaluation metrics and future directions of a question answering system

Qamar, F., Latif, S., & Shah, A. A. (2023) · Knowledge and Information Systems · doi:10.1007/s10115-023-02019-w · Cited by 6

Question Answering Systems Based on Pre-trained Language Models: Recent Progress

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