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Hallucination

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Showing 601–650 of 1816 papers

TitleStatusHype
A Perspective for Adapting Generalist AI to Specialized Medical AI Applications and Their Challenges—0
A Debate-Driven Experiment on LLM Hallucinations and Accuracy—0
Conditional Hallucinations for Image Compression—0
TimeSuite: Improving MLLMs for Long Video Understanding via Grounded TuningCode2
Investigating the Role of Prompting and External Tools in Hallucination Rates of Large Language Models—0
MaCTG: Multi-Agent Collaborative Thought Graph for Automatic Programming—0
Multilingual Hallucination Gaps in Large Language Models—0
AVHBench: A Cross-Modal Hallucination Benchmark for Audio-Visual Large Language Models—0
Leveraging the Domain Adaptation of Retrieval Augmented Generation Models for Question Answering and Reducing Hallucination—0
ProveRAG: Provenance-Driven Vulnerability Analysis with Automated Retrieval-Augmented LLMsCode0
Do Robot Snakes Dream like Electric Sheep? Investigating the Effects of Architectural Inductive Biases on Hallucination—0
GeoCode-GPT: A Large Language Model for Geospatial Code Generation Tasks—0
Privacy-hardened and hallucination-resistant synthetic data generation with logic-solvers—0
IPL: Leveraging Multimodal Large Language Models for Intelligent Product Listing—0
Fine-Tuning Large Language Models to Appropriately Abstain with Semantic Entropy—0
Navigating Noisy Feedback: Enhancing Reinforcement Learning with Error-Prone Language ModelsCode0
SG-FSM: A Self-Guiding Zero-Shot Prompting Paradigm for Multi-Hop Question Answering Based on Finite State Machine—0
Large language models enabled multiagent ensemble method for efficient EHR data labeling—0
Towards a Reliable Offline Personal AI Assistant for Long Duration Spaceflight—0
Mitigating Hallucinations of Large Language Models in Medical Information Extraction via Contrastive Decoding—0
Mitigating Object Hallucination via Concentric Causal AttentionCode2
ToW: Thoughts of Words Improve Reasoning in Large Language ModelsCode0
Can Knowledge Editing Really Correct Hallucinations?Code1
Reducing Hallucinations in Vision-Language Models via Latent Space SteeringCode2
NetSafe: Exploring the Topological Safety of Multi-agent Networks—0
Learning to Generate and Evaluate Fact-checking Explanations with Transformers—0
A Survey of Hallucination in Large Visual Language Models—0
Hallucination Detox: Sensitivity Dropout (SenD) for Large Language Model Training—0
Explaining Graph Neural Networks with Large Language Models: A Counterfactual Perspective for Molecular Property PredictionCode0
Coarse-to-Fine Highlighting: Reducing Knowledge Hallucination in Large Language Models—0
Good Parenting is all you need -- Multi-agentic LLM Hallucination Mitigation—0
ELOQ: Resources for Enhancing LLM Detection of Out-of-Scope QuestionsCode0
Paths-over-Graph: Knowledge Graph Empowered Large Language Model ReasoningCode1
ETF: An Entity Tracing Framework for Hallucination Detection in Code Summaries—0
From Single to Multi: How LLMs Hallucinate in Multi-Document SummarizationCode0
MCQG-SRefine: Multiple Choice Question Generation and Evaluation with Iterative Self-Critique, Correction, and Comparison FeedbackCode0
Mitigating Hallucinations in Large Vision-Language Models via Summary-Guided DecodingCode1
Utilizing Large Language Models in an iterative paradigm with domain feedback for zero-shot molecule optimization—0
FaithBench: A Diverse Hallucination Benchmark for Summarization by Modern LLMsCode1
RosePO: Aligning LLM-based Recommenders with Human Values—0
MMed-RAG: Versatile Multimodal RAG System for Medical Vision Language ModelsCode3
On A Scale From 1 to 5: Quantifying Hallucination in Faithfulness Evaluation—0
When Not to Answer: Evaluating Prompts on GPT Models for Effective Abstention in Unanswerable Math Word Problems—0
Iter-AHMCL: Alleviate Hallucination for Large Language Model via Iterative Model-level Contrastive Learning—0
Graph-constrained Reasoning: Faithful Reasoning on Knowledge Graphs with Large Language ModelsCode3
What Do LLMs Need to Understand Graphs: A Survey of Parametric Representation of Graphs—0
Controlled Automatic Task-Specific Synthetic Data Generation for Hallucination Detection—0
A Claim Decomposition Benchmark for Long-form Answer VerificationCode0
The Curse of Multi-Modalities: Evaluating Hallucinations of Large Multimodal Models across Language, Visual, and AudioCode3
Search Engines in an AI Era: The False Promise of Factual and Verifiable Source-Cited ResponsesCode1
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