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Hallucination

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Showing 1476–1500 of 1816 papers

TitleStatusHype
Evaluating ChatGPT as a Question Answering System: A Comprehensive Analysis and Comparison with Existing Models—0
Context Tuning for Retrieval Augmented Generation—0
DelucionQA: Detecting Hallucinations in Domain-specific Question Answering—0
HALO: An Ontology for Representing and Categorizing Hallucinations in Large Language Models—0
Behind the Magic, MERLIM: Multi-modal Evaluation Benchmark for Large Image-Language ModelsCode0
On Exploring the Reasoning Capability of Large Language Models with Knowledge Graphs—0
How to Build an AI Tutor That Can Adapt to Any Course Using Knowledge Graph-Enhanced Retrieval-Augmented Generation (KG-RAG)—0
Understanding Your Agent: Leveraging Large Language Models for Behavior Explanation—0
Combating the "Sameness" in AI Art: Reflections on the Interactive AI Installation Fencing Hallucination—0
Mitigating Hallucination in Visual Language Models with Visual Supervision—0
Deficiency of Large Language Models in Finance: An Empirical Examination of Hallucination—0
Calibrated Language Models Must Hallucinate—0
Controlling Large Language Model-based Agents for Large-Scale Decision-Making: An Actor-Critic Approach—0
Challenges of Large Language Models for Mental Health Counseling—0
Minimizing Factual Inconsistency and Hallucination in Large Language Models—0
Mitigating Large Language Model Hallucinations via Autonomous Knowledge Graph-based Retrofitting—0
KNVQA: A Benchmark for evaluation knowledge-based VQA—0
Adapting LLMs for Efficient, Personalized Information Retrieval: Methods and Implications—0
Control in Hybrid Chatbots—0
GPT-4V(ision) for Robotics: Multimodal Task Planning from Human Demonstration—0
Chain of Visual Perception: Harnessing Multimodal Large Language Models for Zero-shot Camouflaged Object DetectionCode0
Journey of Hallucination-minimized Generative AI Solutions for Financial Decision Makers—0
Crafting In-context Examples according to LMs' Parametric KnowledgeCode0
Deceptive Semantic Shortcuts on Reasoning Chains: How Far Can Models Go without Hallucination?Code0
How Trustworthy are Open-Source LLMs? An Assessment under Malicious Demonstrations Shows their VulnerabilitiesCode0
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