hallucination
Hallucination in AI refers to instances where a model generates plausible-sounding but factually incorrect or nonsensical outputs. This phenomenon can occur in both language and vision tasks, often highlighting challenges in grounding generated content to reality.
- AVCD: Mitigating Hallucinations in Audio-Visual Large Language Models through Contrastive Decoding
- Bayesian Concept Bottleneck Models with LLM Priors
- CGBench: Benchmarking Language Model Scientific Reasoning for Clinical Genetics Research
- Distilling LLM Agent into Small Models with Retrieval and Code Tools
- Dynamic Bundling with Large Language Models for Zero-Shot Inference on Text-Attributed Graphs
- Generalization or Hallucination? Understanding Out-of-Context Reasoning in Transformers
- HypoBootstrap: A Bootstrapping Framework for Inductive Reasoning
- Intervene-All-Paths: Unified Mitigation of LVLM Hallucinations across Alignment Formats
- MJ-Bench: Is Your Multimodal Reward Model Really a Good Judge for Text-to-Image Generation?
- Mitigating Hallucination in VideoLLMs via Temporal-Aware Activation Engineering
- More Thinking, Less Seeing? Assessing Amplified Hallucination in Multimodal Reasoning Models
- VMDT: Decoding the Trustworthiness of Video Foundation Models