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Evidence record 3779 · automatically gathered

Language Bias in LVLMs: From In-Depth Analysis to Simple and Effective Mitigation

Large Vision-Language Models (LVLMs) extend large language models with visual understanding, but remain vulnerable to hallucination, where outputs are fluent yet inconsistent with images. Recent studies link this issue to language bias-the tendency of LVLMs to over-rely on text while neglecting visual inputs. Yet most analyses remain empirical without uncovering its underlying cause. In this paper, we provide a systematic study of language bias and identify its root in modality misalignment duri

Record details

Published: 24 May 2026
Source: arXiv
Category: Research
Topics: Bias & fairness · Safety & alignment
Retrieved: 14 July 2026

source-onlyevidence status

These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.

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Shared topic: Bias & fairness · Shared topic: Safety & alignment

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How to cite this record

ethics.ai (24 May 2026), “Language Bias in LVLMs: From In-Depth Analysis to Simple and Effective Mitigation,” evidence record 3779, https://ethics.ai/record/3779 (originally published by arXiv).

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