arXiv (OAI Expanded)
open access
FADE: Mitigating Hallucinations by Reducing Language-Prior Dominance in Large Vision-Language Models
Despite the impressive capabilities of Large Vision-Language Models (LVLMs), they remain susceptible to hallucination, generating content inconsistent with the input image. Recent studies attribute this to the dominance of language priors over visual inputs and employ contrastive decoding methods to
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Reducing Hallucination in Multimodal Large Language Models through Hard Grounding Preference Supervision#1116282
- Beyond Language Priors: Diagnosing and Fixing Visual-Origin Hallucinations in Multimodal LLM#819691
- Beyond Language Priors: Diagnosing and Fixing Visual-Origin Hallucinations in Multimodal LLM#809695
- SpanCalib-VLM: Calibrated Hallucination Span Detection in Vision-Language Models#799221
- SpanCalib-VLM: Calibrated Hallucination Span Detection in Vision-Language Models#797798
Record · ID 1123146
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive