On the Origin of Hallucinations in Conversational Models: Is it the Datasets or the Models?

Adam: A Method for Stochastic Optimization

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Sequence Level Training with Recurrent Neural Networks

Ranzato, M., Chopra, S., Auli, M., & Zaremba, W. (2015) · arXiv · doi:10.48550/arxiv.1511.06732 · Cited by 1,426

The Curious Case of Neural Text Degeneration

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“Everyone wants to do the model work, not the data work”: Data Cascades in High-Stakes AI

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Wizard of Wikipedia: Knowledge-Powered Conversational agentsPreprint

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Explainability for Large Language Models: A Survey

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Generative artificial intelligence

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Ojha, A. K., Doğruöz, A. S., Da San Martino, G., Madabushi, H. T., Kumar, R., & Sartori, E. (2015) · doi:10.18653/v1/s15-2 · Cited by 232

Foundation metrics for evaluating effectiveness of healthcare conversations powered by generative AI

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The clinicians’ guide to large language models: a general perspective with a focus on hallucinations

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Marketing with ChatGPT: Navigating the Ethical Terrain of GPT-Based Chatbot Technology

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RARR: Researching and revising what language models say, using language models

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FaithDial : A Faithful Benchmark for Information-Seeking Dialogue

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Assessing AI Simplification of Medical Texts: Readability and Content Fidelity

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Sensemaking with AI: how trust influences human–AI collaboration in health and creative industries

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ChatGPT and oral cancer: a study on informational reliability

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Benchmarking Uncertainty Quantification Methods for Large Language Models with LM-Polygraph

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Software Testing Research Challenges: An Industrial Perspective

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LM-Polygraph: Uncertainty estimation for language models

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Designing LLM Chains by Adapting Techniques from Crowdsourcing Workflows

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The Dangers of trusting Stochastic Parrots: Faithfulness and Trust in Open-domain Conversational Question Answering

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KILM: Knowledge Injection into Encoder-Decoder Language Models

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Studying Psychosis Using Natural Language Generation: A Review of Emerging Opportunities

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Potential safety concerns in use of the artificial intelligence chatbot ‘ChatGPT’ for perioperative patient communication

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Evaluation of Hallucination and Robustness for Large Language Models

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KINet: Incorporating Relevant Facts Into Knowledge-Grounded Dialog Generation

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AutoHall: Automated Factuality Hallucination Dataset Generation for Large Language Models

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

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Generative Design and Experimental Validation of Non-Fullerene Acceptors for Photovoltaics

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Explicit Knowledge Integration for Knowledge-Aware Visual Question Answering about Named Entities

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Goal Awareness for Conversational AI: Proactivity, Non-collaborativity, and Beyond

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Evaluating the Inclusiveness of Artificial Intelligence Software in Enhancing Project Management Efficiency – A review and examples of quantitative measurement methods

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scChat : A large language model‐powered co‐pilot for contextualized single‐cell RNA sequencing analysis

Chiu, H.-H., Varghese, A., Shao, K., Lu, Y., Nahar, R., Chen, H., Deng, Q., Bao, X., & Li, C. (2026) · AIChE Journal · doi:10.1002/aic.70285 · Cited by 7

Explicit Syntactic Guidance for Neural Text Generation

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ProSLM: A Prolog Synergized Language Model for explainable Domain Specific Knowledge Based Question Answering

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It Takes Two to Tango: Navigating Conceptualizations of NLP Tasks and Measurements of Performance

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Chinese Chat Room: AI Hallucinations, Epistemology and Cognition

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Optimizing Datasets for Code Summarization: Is Code-Comment Coherence Enough?

Vitale, A., Mastropaolo, A., Oliveto, R., Penta, M. D., & Scalabrino, S. (2025) · IEEE International Conference on Program Comprehension (ICPC) · doi:10.1109/icpc66645.2025.00033 · Cited by 8

Towards Faithful Dialogues via Focus Learning

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Exploring In-Context Learning for Knowledge Grounded Dialog Generation

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A Survey on Using Linguistic Markers for Diagnosing Neuropsychiatric Disorders with Artificial Intelligence

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Correcting Factuality Hallucination in Complaint Large Language Model via Entity-Augmented

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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

Koala: An Index for Quantifying Overlaps with Pre-training Corpora

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Establishing Trustworthiness: Rethinking Tasks and Model Evaluation

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Conditioning on Dialog Acts improves Empathy Style Transfer

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

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A Spatiotemporal–Semantic Coupling Intelligent Q&A Method for Land Use Approval Based on Knowledge Graphs and Intelligent Agents

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Hallucinations

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LINGUO-COGNITIVE MARKERS IN HUMAN VS AI TEXT ATTRIBUTION: A CASE STUDY OF NARRATIVE AND DESCRIPTIVE DISCOURSE

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Are You Sure About That: Eliciting Natural Language Confidence Indicators from Chatbots

Toney, A., & Singh, L. (2024) · International Conference on Human-Agent Interaction (HAI) · doi:10.1145/3687272.3688318 · Cited by 4

Graph-Based Grounding in a Conversational Clinical Decision Support System

Barham, S., Kelbaugh, M. T., Reddy, A., Ramsden, D., Chandler, C., Tobin-Williams, A., Elhadad, B. S., Cooper, G. R., & Alexander, K. (2024) · IEEE International Conference on Assured Autonomy (ICAA) · doi:10.1109/icaa64256.2024.00014 · Cited by 2

Investigation of the Inference Capabilities and Memorization of Pre-trained Language Models

Sakai, Y., Kamigaito, H., Hayashi, K., & Watanabe, T. (2024) · Journal of Natural Language Processing · doi:10.5715/jnlp.31.1427 · Cited by 2

AI in the city: impact assessment of artificial intelligence uses in earth observation

Šćepanović, S., Bogucka, E. P., & Quercia, D. (2025) · Urban Informatics · doi:10.1007/s44212-025-00074-y · Cited by 3

Exploring Accurate and Generic Simile Knowledge from Pre-trained Language Models

Zhou, S., Ma, L., & Shao, Y. (2023) · Lecture notes in computer science · doi:10.1007/978-981-99-6207-5_22 · Cited by 1

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Optimizing Access to Interoperability Resources in Mobility Through Context-Aware Large Language Models (LLMs)

Mandapati, S. V., Kummetha, V. C., Concas, S., & Staes, L. (2025) · Electronics · doi:10.3390/electronics15010152 · Cited by 1

A White-Box Benchmark for Intrinsic Hallucination Detection in Turkish LLMs

Bayramoglu, B., Ficici, M., Bildirici, F., Kızıltepe, R. S., & Karakuş, M. (2026) · International Informatics and Software Engineering Conference (IISEC) · doi:10.1109/iisec69317.2026.11418463 · Cited by 1

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Truong, V. Q., & Ritchie, M. D. (2026) · Briefings in Bioinformatics · doi:10.1093/bib/bbag256 · Cited by 1

Reranking for a Polish Medical Search Engine

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Data Disquiet: Concerns about the Governance of Data for Generative AIPreprint

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Standardized Nomenclature Prompting (SNP): Enhancing Medical Queries in Generative Language Models (Preprint)Preprint

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Graph vs. Sequence: An Empirical Study on Knowledge Forms for Knowledge-Grounded Dialogue

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Post-hoc Utterance Refining Method by Entity Mining for Faithful Knowledge Grounded Conversations

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HALP: Detecting Hallucinations in Vision-Language Models without Generating a Single Token

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Alleviating Hallucinations in Large Language Models through Multi-Model Contrastive Decoding and Dynamic Hallucination Detection

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Layer-Wise Localization and Classification of Hallucinations in Large Language Models

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