Contrastive Learning Reduces Hallucination in Conversations

Language Models are Few-Shot Learners

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A simple framework for contrastive learning of visual representations

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BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension

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mixup: Beyond Empirical Risk Minimization

Zhang, H., Cisse, M., Dauphin, Y. N., & Lopez-Paz, D. (2017) · arXiv · doi:10.48550/arxiv.1710.09412 · Cited by 5,831 · see citing works

Survey of Hallucination in Natural Language Generation

Ji, Z., Lee, N., Frieske, R., Yu, T., Su, D., Xu, Y., Ishii, E., Bang, Y. J., Madotto, A., & Fung, P. (2023) · ACM Computing Surveys · doi:10.1145/3571730 · Cited by 5,025 · see citing works

Learning a Similarity Metric Discriminatively, with Application to Face Verification

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Bleu: a method for automatic evaluation of machine translation

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Dense passage retrieval for open-domain question answering

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SimCSE: Simple Contrastive Learning of Sentence Embeddings

Gao, T., Yao, X., & Chen, D. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2021.emnlp-main.552 · Cited by 2,851 · see citing works

Meteor Universal: Language Specific Translation Evaluation for Any Target Language

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Language Models as Knowledge Bases?

Petroni, F., Rocktäschel, T., Riedel, S., Lewis, P., Bakhtin, A., Wu, Y., & Miller, A. (2019) · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) · doi:10.18653/v1/d19-1250 · Cited by 1,834 · see citing works

Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism

Shoeybi, M., Patwary, M., Puri, R., LeGresley, P., Casper, J., & Catanzaro, B. (2019) · arXiv · doi:10.48550/arxiv.1909.08053 · Cited by 893 · see citing works

Recipes for Building an Open-Domain Chatbot

Roller, S., Dinan, E., Goyal, N., Ju, D., Williamson, M., Liu, Y., Xu, J., Ott, M., Smith, E. M., Boureau, Y.-L., et al. (2021) · Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume · doi:10.18653/v1/2021.eacl-main.24 · Cited by 784 · see citing works

LaMDA: Language Models for Dialog Applications

Thoppilan, R., De Freitas, D., Hall, J., Shazeer, N., Kulshreshtha, A., Cheng, H.-T., Jin, A., Bos, T., Baker, L., Du, Y., et al. (2022) · arXiv · doi:10.48550/arxiv.2201.08239 · Cited by 778 · see citing works

How Much Knowledge Can You Pack into the Parameters of a Language Model?

Roberts, A., Raffel, C., & Shazeer, N. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.437 · Cited by 638 · see citing works

Towards a Human-like Open-Domain Chatbot

Adiwardana, D., Luong, M.-T., So, D. R., Hall, J., Fiedel, N., Thoppilan, R., Yang, Z., Kulshreshtha, A., Nemade, G., Lu, Y., et al. (2020) · arXiv · doi:10.48550/arxiv.2001.09977 · Cited by 614 · see citing works

Wizard of Wikipedia: Knowledge-Powered Conversational agentsPreprint

Dinan, E., Roller, S., Shuster, K., Fan, A., Auli, M., & Weston, J. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1811.01241 · Cited by 610 · see citing works

Retrieval Augmentation Reduces Hallucination in Conversation

Shuster, K., Poff, S., Chen, M., Kiela, D., & Weston, J. (2021) · Findings of the Association for Computational Linguistics: EMNLP 2021 · doi:10.18653/v1/2021.findings-emnlp.320 · Cited by 575 · see citing works

Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrieval

Xiong, L., Xiong, C., Li, Y., Tang, K., Liu, J., Bennett, P. N., Ahmed, J., & Overwikj, A. (2021) · International Conference on Learning Representations · Cited by 376 · see citing works

KILT: a Benchmark for Knowledge Intensive Language Tasks

Petroni, F., Piktus, A., Fan, A., Lewis, P., Yazdani, M., De Cao, N., Thorne, J., Jernite, Y., Karpukhin, V., Maillard, J., et al. (2021) · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · doi:10.18653/v1/2021.naacl-main.200 · Cited by 357 · see citing works

Autoregressive Entity RetrievalPreprint

Cao, N. D., Izacard, G., Riedel, S., & Petroni, F. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.00904 · Cited by 307 · see citing works

Challenges in building intelligent open-domain dialog systems

Huang, M., Zhu, X., & Gao, J. (2020) · ACM Transactions on Information Systems · doi:10.1145/3383123 · Cited by 288 · see citing works

Neural Text Generation with Unlikelihood Training

Welleck, S., Kulikov, I., Roller, S., Dinan, E., Cho, K., & Weston, J. (2019) · arXiv · doi:10.48550/arxiv.1908.04319 · Cited by 254 · see citing works

Knowledge-Grounded Dialogue Generation with Pre-trained Language ModelsPreprint

Zhao, X., Wu, W., Xu, C., Tao, C., Zhao, D.-Y., & Yan, R. (2020) · Conference on Empirical Methods in Natural Language Processing · doi:10.48550/arxiv.2010.08824 · Cited by 215 · see citing works

Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrieval

Xiong, L., Xiong, C., Li, Y., Tang, K.-F., Liu, J., Bennett, P., Ahmed, J., & Overwijk, A. (2020) · arXiv · doi:10.48550/arxiv.2007.00808 · Cited by 204 · see citing works

Recall and Learn: Fine-tuning Deep Pretrained Language Models with Less Forgetting

Chen, S., Hou, Y., Cui, Y., Che, W., Liu, T., & Yu, X. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.634 · Cited by 181 · see citing works

Knowledge-Grounded Dialogue Generation with Pre-trained Language Models

Zhao, X., Wu, W., Xu, C., Tao, C., Zhao, D., & Yan, R. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.272 · Cited by 168 · see citing works

Knowledge Diffusion for Neural Dialogue Generation

Liu, S., Chen, H., Ren, Z., Feng, Y., Liu, Q., & Yin, D. (2018) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p18-1138 · Cited by 166 · see citing works

Evaluating Commonsense in Pre-Trained Language Models

Zhou, X., Zhang, Y., Cui, L., & Huang, D. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i05.6523 · Cited by 153 · see citing works

CLIFF: Contrastive Learning for Improving Faithfulness and Factuality in Abstractive Summarization

Cao, S., & Wang, L. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.532 · Cited by 135 · see citing works

Don’t Say That! Making Inconsistent Dialogue Unlikely with Unlikelihood Training

Li, M., Roller, S., Kulikov, I., Welleck, S., Boureau, Y.-L., Cho, K., & Weston, J. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.428 · Cited by 128 · see citing works

On Exposure Bias, Hallucination and Domain Shift in Neural Machine Translation

Wang, C., & Sennrich, R. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.326 · Cited by 118 · see citing works

Neural Path Hunter: Reducing Hallucination in Dialogue Systems via Path Grounding

Dziri, N., Madotto, A., Zaïane, O., & Bose, A. J. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2021.emnlp-main.168 · Cited by 81 · see citing works

Improved Natural Language Generation via Loss Truncation

Kang, D. D., & Hashimoto, T. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.66 · Cited by 71 · see citing works

MixCo: Mix-up Contrastive Learning for Visual RepresentationPreprint

Kim, S., Lee, G., Bae, S., & Yun, S.-Y. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.06300 · Cited by 64 · see citing works

Learning Knowledge Bases with Parameters for Task-Oriented Dialogue Systems

Madotto, A., Cahyawijaya, S., Winata, G. I., Xu, Y., Liu, Z., Lin, Z., & Fung, P. (2020) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2020.findings-emnlp.215 · Cited by 63 · see citing works

Negative Training for Neural Dialogue Response Generation

He, T., & Glass, J. R. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.185 · Cited by 54 · see citing works

Multi-Stage Prompting for Knowledgeable Dialogue Generation

Liu, Z., Patwary, M. A., Prenger, R., Prabhumoye, S., Ping, W., Shoeybi, M., & Catanzaro, B. (2022) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2022.findings-acl.104 · Cited by 47 · see citing works

Substructure Substitution: Structured Data Augmentation for NLP

Shi, H., Livescu, K., & Gimpel, K. (2021) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2021.findings-acl.307 · Cited by 41 · see citing works

Exploring Prompt-based Few-shot Learning for Grounded Dialog GenerationPreprint

Zheng, C., & Huang, M. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2109.06513 · Cited by 34 · see citing works

Augmenting End-to-End Dialog Systems with Commonsense KnowledgePreprint

Young, T., Cambria, E., Chaturvedi, I., Huang, M., Zhou, H., & Biswas, S. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1709.05453 · Cited by 30 · see citing works

Retrieval-Free Knowledge-Grounded Dialogue Response Generation with Adapters

Xu, Y., Ishii, E., Cahyawijaya, S., Liu, Z., Winata, G. I., Madotto, A., Su, D., & Fung, P. (2022) · DialDoc Workshop on Document-grounded Dialogue and Conversational Question Answering (DialDoc) · doi:10.18653/v1/2022.dialdoc-1.10 · Cited by 28 · see citing works

Knowledge Enhanced Fine-Tuning for Better Handling Unseen Entities in Dialogue Generation

Cui, L., Wu, Y., Liu, S., & Zhang, Y. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.179 · Cited by 21 · see citing works

Retrieval-Free Knowledge-Grounded Dialogue Response Generation with Adapters

Xu, Y., Ishii, E., Cahyawijaya, S., Liu, Z., Winata, G. I., Madotto, A., Su, D., & Fung, P. (2021) · arXiv · doi:10.48550/arxiv.2105.06232 · Cited by 17 · see citing works

TreeMix: Compositional Constituency-based Data Augmentation for Natural Language Understanding

Zhang, L., Yang, Z., & Yang, D. (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.385 · Cited by 16 · see citing works

On Exposure Bias, Hallucination and Domain Shift in Neural Machine Translation

Wang, C., & Sennrich, R. (2020) · arXiv · doi:10.48550/arxiv.2005.03642 · Cited by 12 · see citing works

Diversifying Neural Dialogue Generation via Negative Distillation

Li, Y., Feng, S., Sun, B., & Li, K. (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.31 · Cited by 10 · see citing works

Are Pre-trained Language Models Knowledgeable to Ground Open Domain Dialogues?Preprint

Zhao, Y., Wu, W., & Xu, C. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2011.09708 · Cited by 10 · see citing works

A Simple Contrastive Learning Objective for Alleviating Neural Text DegenerationPreprint

Jiang, S., Zhang, R., Vakulenko, S., & de Rijke, M. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2205.02517 · Cited by 10 · see citing works

Lexical Knowledge Internalization for Neural Dialog Generation

Wu, Z., Bi, W., Li, X., Kong, L., & Kao, B. (2022) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2022.acl-long.547 · Cited by 5 · see citing works

Enhancing Dialogue Generation via Multi-Level Contrastive LearningPreprint

Li, X., Li, P., Wang, Y., Liu, X., & Lam, W. P. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2009.09147 · Cited by 4 · see citing works

Neural Text Generation with Artificial Negative ExamplesPreprint

Shirai, K., Hashimoto, K., Eriguchi, A., Ninomiya, T., & Mori, S. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.14124 · Cited by 3 · see citing works

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

HaluEval: A Large-Scale Hallucination Evaluation Benchmark for Large Language Models

Li, J., Cheng, X., Zhao, X., Nie, J.-Y., & Wen, J.-R. (2023) · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2023.emnlp-main.397 · Cited by 343 · see citing works

Chain-of-Verification Reduces Hallucination in Large Language Models

Dhuliawala, S., Komeili, M., Xu, J., Raileanu, R., Li, X., Celikyilmaz, A., & Weston, J. (2024) · Findings of the Association for Computational Linguistics ACL 2024 · doi:10.18653/v1/2024.findings-acl.212 · Cited by 282 · see citing works

AI agents under threat: A survey of key security challenges and future pathways

Deng, Z., Guo, Y., Han, C., Ma, W., Xiong, J., Wen, S., & Xiang, Y. (2025) · ACM Computing Surveys · doi:10.1145/3716628 · Cited by 210 · see citing works

Beware of botshit: How to manage the epistemic risks of generative chatbots

Hannigan, T. R., McCarthy, I. P., & Spicer, A. (2024) · Business Horizons · doi:10.1016/j.bushor.2024.03.001 · Cited by 175 · see citing works

Towards trustworthy LLMs: a review on debiasing and dehallucinating in large language models

Lin, Z., Guan, S., Zhang, W., Zhang, H., Li, Y., & Zhang, H. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10896-y · Cited by 141 · see citing works

MASTERKEY: Automated Jailbreaking of Large Language Model Chatbots

Deng, G., Liu, Y., Li, Y., Wang, K., Zhang, Y., Li, Z., Wang, H., Zhang, T., & Liu, Y. (2024) · Network and Distributed System Security Symposium · doi:10.14722/ndss.2024.24188 · Cited by 126 · see citing works

Prompt Injection attack against LLM-integrated Applications

Liu, Y., Deng, G., Li, Y., Wang, K., Wang, Z., Wang, X., Zhang, T., Liu, Y., Wang, H., Zheng, Y., et al. (2023) · arXiv · doi:10.48550/arxiv.2306.05499 · Cited by 100 · see citing works

Applications of LLMs for Generating Cyber Security Exercise Scenarios

Mudassar Yamin, M., Hashmi, E., Ullah, M., & Katt, B. (2024) · IEEE Access · doi:10.1109/access.2024.3468914 · Cited by 91 · see citing works

Evaluating the Utilities of Foundation Models in Single‐Cell Data Analysis

Liu, T., Li, K., Wang, Y., Li, H., & Zhao, H. (2026) · Advanced Science · doi:10.1002/advs.202514490 · Cited by 76 · see citing works

Evaluating the Utilities of Foundation Models in Single-cell Data Analysis

Liu, T., Li, K., Wang, Y., Li, H., & Zhao, H. (2023) · doi:10.1101/2023.09.08.555192 · Cited by 62 · see citing works

Quality, Accuracy, and Bias in ChatGPT-Based Summarization of Medical Abstracts

Hake, J., Crowley, M., Coy, A., Shanks, D., Eoff, A., Kirmer-Voss, K., Dhanda, G., & Parente, D. J. (2024) · The Annals of Family Medicine · doi:10.1370/afm.3075 · Cited by 47 · see citing works

Chain-of-Verification Reduces Hallucination in Large Language Models

Dhuliawala, S., Komeili, M., Xu, J., Raileanu, R., Li, X., Celikyilmaz, A., & Weston, J. (2023) · arXiv · doi:10.48550/arxiv.2309.11495 · Cited by 42 · see citing works

Generative Artificial Intelligence and Large Language Models in Primary Care Medical Education

Parente, D. J. (2024) · Family Medicine · doi:10.22454/fammed.2024.775525 · Cited by 40 · see citing works

Safeguarding human values: rethinking US law for generative AI’s societal impacts

Cheong, I., Caliskan, A., & Kohno, T. (2024) · AI and Ethics · doi:10.1007/s43681-024-00451-4 · Cited by 39 · see citing works

Student Perspectives on Using a Large Language Model (LLM) for an Assignment on Professional Ethics

Grande, V., Kiesler, N., & Rodríguez, M. A. F. (2024) · ACM Conference on Innovation and Technology in Computer Science Education (ITiCSE) · doi:10.1145/3649217.3653624 · Cited by 35 · see citing works

Survey on Factuality in Large Language Models

Wang, C., Liu, X., Yue, Y., Guo, Q., Hu, X., Tang, X., Zhang, T., Cheng, J., Yao, Y., Hu, X., et al. (2025) · ACM Computing Surveys · doi:10.1145/3742420 · Cited by 221 · see citing works

Strategies for the Analysis and Elimination of Hallucinations in Artificial Intelligence Generated Medical Knowledge

Chen, F., Li, Y., Chen, Y.-L., Bian, Z.-X., Duo, L., Zhou, Q., & Zhang, L. (2025) · Journal of Evidence-Based Medicine · doi:10.1111/jebm.70075 · Cited by 24 · see citing works

A prompt construction method for the reverse dictionary task of large-scale language models

Tian, S., Huang, S., Li, R., & Wei, C. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.108596 · Cited by 19 · see citing works

Addressing Compiler Errors: Stack Overflow or Large Language Models?Preprint

Widjojo, P., & Treude, C. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4529345 · Cited by 16 · see citing works

GIA: LLM-Enabled Generative Intent Abstraction to Enhance Adaptability for Intent-Driven Networks

Kou, S., Kou, S., Yang, C., & Gurusamy, M. (2025) · IEEE Transactions on Cognitive Communications and Networking · doi:10.1109/tccn.2025.3545589 · Cited by 14 · see citing works

Infusing internalized knowledge of language models into hybrid prompts for knowledgeable dialogue generation

Bai, J., Yan, Z., Zhang, S., Yang, J., Guo, H., & Li, Z. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.111874 · Cited by 12 · see citing works

An integrated framework for Gen AI-assisted management learning: Insights from Kolb's learning cycle theory and knowledge types perspectives

Lee, K. (2025) · The International Journal of Management Education · doi:10.1016/j.ijme.2025.101164 · Cited by 13 · see citing works

Mitigating hallucinations in healthcare AI: a systematic review of evidence-based strategies

Basu, S., & Huynh, B. Q. (2026) · BMC Health Services Research · doi:10.1186/s12913-026-14851-1 · Cited by 11 · see citing works

Automatic Detection of Inconsistencies and Hierarchical Topic Classification for Open-Domain Chatbots

Rodríguez-Cantelar, M., Estecha-Garitagoitia, M., D’Haro, L. F., Matı́a, F., & de Córdoba, R. (2023) · Applied Sciences · doi:10.3390/app13169055 · Cited by 10 · see citing works

A Lifecycle-Oriented Survey of Emerging Threats and Vulnerabilities in Large Language Models

de Maio, C., Di Gisi, M., Fenza, G., Gallo, M., & Loia, V. (2025) · IEEE Access · doi:10.1109/access.2025.3619764 · Cited by 10 · see citing works

Multi-granularity contrastive zero-shot learning model based on attribute decomposition

Wang, Y., Wang, J., Fan, Y., Chai, Q., Zhang, H., Li, X., & Li, R. (2024) · Information Processing & Management · doi:10.1016/j.ipm.2024.103898 · Cited by 8 · see citing works

Has OpenAI Achieved Artificial General Intelligence in ChatGPT?

Williams, A. E. (2023) · Artificial Intelligence and Applications · doi:10.47852/bonviewaia3202751 · Cited by 8 · see citing works

Comprehensive Evaluation of AI Hallucination and Novel UV-Oriented Framework toward Safe and Trustworthy AI

Liu, Z., Kou, J., Zhang, W., Gu, C., Fang, X., Huang, Z., Hao, Y., Li, H., Lu, X., Yin, A., et al. (2024) · International Conference on Universal Village (UV) · doi:10.1109/uv63228.2024.11189137 · Cited by 9 · see citing works

Towards a Unified Framework for Reference Retrieval and Related Work Generation

Shi, Z., Gao, S., Zhang, Z., Chen, X., Chen, Z., Ren, P., & Ren, Z. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.385 · Cited by 8 · see citing works

Evaluating the Utilities of Large Language Models in Single-cell Data AnalysisPreprint

Zhao, H., Liu, T., Li, K., Wang, Y., & Li, H. (2023) · Research Square · doi:10.21203/rs.3.rs-3376641/v1 · Cited by 7 · see citing works

Seeing the Forest from the Trees: Unveiling the Landscape of Generative AI for Education Through Six Evaluation Dimensions

Feldman-Maggor, Y., Pargman, T. C., & Viberg, O. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72312-4_12 · Cited by 6 · see citing works

Complex QA and language models hybrid architectures, SurveyPreprint

Daull, X., Bellot, P., Bruno, E., Martin, V., & Murisasco, É. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2302.09051 · Cited by 17 · see citing works

Large Language Models Predict Transportation Mode Choice Behavior for a Variety of Alternative Sets

Nishida, R., Ishigaki, T., & Onishi, M. (2025) · Transportation Research Record Journal of the Transportation Research Board · doi:10.1177/03611981251352499 · Cited by 5 · see citing works

The rise of hallucination in large language models: systematic reviews, performance analysis and challenges

Abdullahi, S., Danyaro, K. U., & Chiroma, H. (2026) · Cluster Computing · doi:10.1007/s10586-025-05891-z · Cited by 7 · see citing works

M3TQA: Multi-View, Multi-Hop and Multi-Stage Reasoning for Temporal Question Answering

Zha, Z., Qi, P., Bao, X., Tian, M., & Qin, B. (2024) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp48485.2024.10448071 · Cited by 5 · see citing works

IllusionX: An LLM-powered mixed reality personal companionPreprint

Yousri, R., Essam, Z., Kareem, Y., Sherief, Y., Gamil, S., & Safwat, S. (2024) · doi:10.36227/techrxiv.170775603.33253596/v1 · Cited by 7 · see citing works

Multi-view adaptive contrastive learning for information retrieval based fault localization

Zhou, C., Xie, X., Gong, C., He, P. J., & Li, B. (2025) · Automated Software Engineering · doi:10.1007/s10515-025-00573-x · Cited by 4 · see citing works

Towards Faithful Dialogues via Focus Learning

Deng, Y., Zhang, X., Huang, H., & Hu, Y. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.250 · Cited by 4 · see citing works

A Survey of Hallucination in Large Language Models

何, 金. (2026) · Artificial Intelligence and Robotics Research · doi:10.12677/airr.2026.151016 · Cited by 3 · see citing works

Towards Fewer Hallucinations in Knowledge-Grounded Dialogue Generation via Augmentative and Contrastive Knowledge-Dialogue

Sun, B., Li, Y., Mi, F., Bie, F., Li, Y., & Li, K. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-short.148 · Cited by 3 · see citing works

Answering Ambiguous Questions via Iterative Prompting

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Exploring the Synergy of Grammar-Aware Prompt Engineering and Formal Methods for Mitigating Hallucinations in LLMs

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Rethinking Hallucinations: A Cognitive-Inspired Taxonomy and Comprehensive Survey in Large Language Models, Large Vision-Language Models, and Multimodal Large Language ModelsPreprint

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A framework to synthetically generate fine-grained hallucinated data

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Beyond Isolated Fixes: A Comprehensive Survey on Hallucination Mitigation with a Three-Dimensional Taxonomy and Integrative Framework

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DialFill: Utilizing Dialogue Filling to Integrate Retrieved Knowledge in Responses

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Adversarial hard negative samples for continual relation extraction

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Oreo: A Plug-in Context Reconstructor to Enhance Retrieval-Augmented Generation

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Gemini's Multimodal Prowess: Robust Detection of Adversarial Patch Attacks through Text and Image Inputs

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EMCRL: EM-Enhanced Negative Sampling Strategy for Contrastive Representation Learning

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PRISM-Med: Parameter-Efficient Robust Interdomain Specialty Model for Medical Language Tasks

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EFGCLS: A Cross-Lingual Summarization Method Based on Element Fact-Relationship Generation

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EMPOWER-KARE: Deep Prompt Learning for Knowledge-Aware Response Generation in Clinical Counseling and Legal Support Conversations

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PredictQA: A Novel Approach to Predictive Temporal Knowledge Graph Question Answering

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From ConOps to an Operational Domain Model: Harnessing LLMs for Conceptual Model Design

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The Scope of In-Context Learning for the Extraction of Medical Temporal Constraints

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Can Hallucination Reduction in LLMs Improve Online Sexism Detection?

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Comprehending and Reducing LLM Hallucinations

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Exploring the Knowledge Mismatch Hypothesis: Hallucination Propensity in Small Models Fine-tuned on Data from Larger Models

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Adaptive Learning Preference Rectification For Knowledge-Grounded Dialogue Generation with HallucinationsPreprint

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Freehal: A Framework for Editing Large Language Model HallucinationsPreprint

Lim, J., Shin, J., Lee, Y., & Song, H.-J. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4893610

Enhancing the Completeness of Rationales for Multi-Step Question Answering

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Self-Supervised Neural Architecture Search for Multimodal Deep Neural Networks

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Constraint-Awareness and Graph Reasoning for Temporal Question Answering

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Enhancing human-AI collaboration in game testing: the application of AI in identifying game defects

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ACCon: Angle-Compensated Contrastive Regularizer for Deep Regression

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Threat Intelligence Entity Recognition Based on Large Language Model with Contrastive Learning

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Reframing Hallucination in Large Language Models: A Lifecycle-Based, Mechanism-Aligned, and Phenomenon-Consistent Definition

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Skills Training in the Age of AI

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Temporal-Aware Heterogeneous Graph Reasoning with Multi-view Fusion for Temporal Question Answering

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Hallucination Detection and Reduction in Large Language Models: A Practical Study

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Hallucinations in LLMs: a lifecycle-based survey of causes, detection, mitigation, and prevention

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Alucinações em Modelos de Linguagem de Grande Escala: Uma Revisão Sistemática da Literatura

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Iter-AHMCL: Alleviate Hallucination for Large Language Model via Iterative Model-level Contrastive LearningPreprint

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DHI: Leveraging Diverse Hallucination Induction for Enhanced Contrastive Factuality Control in Large Language Models

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HalluCon: Detecting Hallucinations in Question-Answer Systems Using Contrastive Learning

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Retrieval Augmentation Reduces Hallucination in ConversationPreprint

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ContrastSFT: Contrastive Logit Regularization Supervised Fine-Tuning for Mitigating Hallucinations in Large Language Models

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