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Hallucination

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Showing 1401–1425 of 1816 papers

TitleStatusHype
Vision-Flan: Scaling Human-Labeled Tasks in Visual Instruction Tuning—0
Measuring and Reducing LLM Hallucination without Gold-Standard Answers—0
Trading off Consistency and Dimensionality of Convex Surrogates for the Mode—0
Towards Uncovering How Large Language Model Works: An Explainability Perspective—0
LLMs in the Heart of Differential Testing: A Case Study on a Medical Rule Engine—0
Comparing Hallucination Detection Metrics for Multilingual Generation—0
Using Hallucinations to Bypass GPT4's Filter—0
Retrieve Only When It Needs: Adaptive Retrieval Augmentation for Hallucination Mitigation in Large Language Models—0
Do LLMs Know about Hallucination? An Empirical Investigation of LLM's Hidden States—0
Visually Dehallucinative Instruction Generation: Know What You Don't KnowCode0
Large Language Model with Graph Convolution for Recommendation—0
LLMAuditor: A Framework for Auditing Large Language Models Using Human-in-the-Loop—0
Visually Dehallucinative Instruction GenerationCode0
A Systematic Review of Data-to-Text NLG—0
Mitigating Object Hallucination in Large Vision-Language Models via Classifier-Free Guidance—0
Careless Whisper: Speech-to-Text Hallucination HarmsCode0
GLaM: Fine-Tuning Large Language Models for Domain Knowledge Graph Alignment via Neighborhood Partitioning and Generative Subgraph Encoding—0
ViGoR: Improving Visual Grounding of Large Vision Language Models with Fine-Grained Reward ModelingCode0
An Examination on the Effectiveness of Divide-and-Conquer Prompting in Large Language Models—0
The Instinctive Bias: Spurious Images lead to Illusion in MLLMsCode0
Enhancing Retrieval Processes for Language Generation with Augmented Queries—0
Improving Assessment of Tutoring Practices using Retrieval-Augmented Generation—0
Aligner: Efficient Alignment by Learning to Correct—0
A Closer Look at the Limitations of Instruction Tuning—0
CorpusLM: Towards a Unified Language Model on Corpus for Knowledge-Intensive Tasks—0
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