ViLBias: Detecting and Reasoning about Bias in Multimodal Content

• URL: http://arxiv.org/abs/2412.17052v5
• Date: Fri, 03 Oct 2025 11:22:35 GMT
• Title: ViLBias: Detecting and Reasoning about Bias in Multimodal Content
• Authors: Shaina Raza, Caesar Saleh, Azib Farooq, Emrul Hasan, Franklin Ogidi, Maximus Powers, Veronica Chatrath, Marcelo Lotif, Karanpal Sekhon, Roya Javadi, Haad Zahid, Anam Zahid, Vahid Reza Khazaie, Zhenyu Yu,
• Abstract summary: ViLBias is a framework for detecting and reasoning about bias in multimodal news.<n> dataset comprises 40,945 text--image pairs from diverse outlets.<n>Results show that images alongside text improves detection accuracy by 3--5%.
• Score: 6.710013984078675
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Detecting bias in multimodal news requires models that reason over text--image pairs, not just classify text. In response, we present ViLBias, a VQA-style benchmark and framework for detecting and reasoning about bias in multimodal news. The dataset comprises 40,945 text--image pairs from diverse outlets, each annotated with a bias label and concise rationale using a two-stage LLM-as-annotator pipeline with hierarchical majority voting and human-in-the-loop validation. We evaluate Small Language Models (SLMs), Large Language Models (LLMs), and Vision--Language Models (VLMs) across closed-ended classification and open-ended reasoning (oVQA), and compare parameter-efficient tuning strategies. Results show that incorporating images alongside text improves detection accuracy by 3--5\%, and that LLMs/VLMs better capture subtle framing and text--image inconsistencies than SLMs. Parameter-efficient methods (LoRA/QLoRA/Adapters) recover 97--99\% of full fine-tuning performance with $<5\%$ trainable parameters. For oVQA, reasoning accuracy spans 52--79\% and faithfulness 68--89\%, both improved by instruction tuning; closed accuracy correlates strongly with reasoning ($r = 0.91$). ViLBias offers a scalable benchmark and strong baselines for multimodal bias detection and rationale quality.

Related papers

• Learning to Trust the Crowd: A Multi-Model Consensus Reasoning Engine for Large Language Models [0.0]
  Large language models (LLMs) achieve strong aver- age performance yet remain unreliable at the instance level.<n>We introduce a Multi-Model Consensus Reasoning Engine that treats the set of LLM outputs as input to a supervised meta-learner.<n>The system maps natural language responses into structured features using semantic embeddings, pairwise similarity and clustering statistics, lexical and structural cues, reasoning-quality scores, confidence estimates, and model-specific priors.
  arXiv  Detail & Related papers  (2026-01-12T06:27:06Z)
• Comparison of Text-Based and Image-Based Retrieval in Multimodal Retrieval Augmented Generation Large Language Model Systems [0.0]
  We present a comparative analysis of two retrieval approaches for multimodal RAG systems.<n>Direct multimodal embedding retrieval significantly outperforms LLM-summary-based approaches.
  arXiv  Detail & Related papers  (2025-11-20T18:56:49Z)
• RepreGuard: Detecting LLM-Generated Text by Revealing Hidden Representation Patterns [50.401907401444404]
  Large language models (LLMs) are crucial for preventing misuse and building trustworthy AI systems.<n>We propose RepreGuard, an efficient statistics-based detection method.<n> Experimental results show that RepreGuard outperforms all baselines with average 94.92% AUROC on both in-distribution (ID) and OOD scenarios.
  arXiv  Detail & Related papers  (2025-08-18T17:59:15Z)
• Multimodal LLMs as Customized Reward Models for Text-to-Image Generation [60.164968941945645]
  We introduce LLaVA-Reward, an efficient reward model designed to automatically evaluate text-to-image (T2I) generations across multiple perspectives.<n>LLaVA-Reward directly utilizes the hidden states of multimodal large language models (MLLMs)<n>We train LLaVA-Reward on four evaluation perspectives: text-image alignment, fidelity/artifact, safety, and overall ranking.
  arXiv  Detail & Related papers  (2025-07-28T23:52:53Z)
• Can Multimodal Large Language Models Understand Spatial Relations? [16.76001474065412]
  We introduce SpatialMQA, a human-annotated spatial relation reasoning benchmark based on COCO 2017.<n>Results indicate that the current state-of-the-art MLLM achieves only 48.14% accuracy, far below the human-level accuracy of 98.40%.
  arXiv  Detail & Related papers  (2025-05-25T07:37:34Z)
• MLLMs are Deeply Affected by Modality Bias [158.64371871084478]
  Recent advances in Multimodal Large Language Models (MLLMs) have shown promising results in integrating diverse modalities such as texts and images.<n>MLLMs are heavily influenced by modality bias, often relying on language while under-utilizing other modalities like visual inputs.<n>This paper argues that MLLMs are deeply affected by modality bias, highlighting its manifestations across various tasks.
  arXiv  Detail & Related papers  (2025-05-24T11:49:31Z)
• SPARC: Score Prompting and Adaptive Fusion for Zero-Shot Multi-Label Recognition in Vision-Language Models [74.40683913645731]
  Zero-shot multi-label recognition (MLR) with Vision-Language Models (VLMs) faces significant challenges without training data, model tuning, or architectural modifications.<n>Our work proposes a novel solution treating VLMs as black boxes, leveraging scores without training data or ground truth.<n>Analysis of these prompt scores reveals VLM biases and AND''/OR' signal ambiguities, notably that maximum scores are surprisingly suboptimal compared to second-highest scores.
  arXiv  Detail & Related papers  (2025-02-24T07:15:05Z)
• AlignVLM: Bridging Vision and Language Latent Spaces for Multimodal Understanding [63.09928907734156]
  AlignVLM is a vision-text alignment method that maps visual features to a weighted average of text embeddings. Our experiments show that AlignVLM achieves state-of-the-art performance compared to prior alignment methods.
  arXiv  Detail & Related papers  (2025-02-03T13:34:51Z)
• Toward Robust Hyper-Detailed Image Captioning: A Multiagent Approach and Dual Evaluation Metrics for Factuality and Coverage [50.84150600032693]
  Multimodal large language models (MLLMs) excel at generating highly detailed captions but often produce hallucinations.<n>We propose a multiagent approach that leverages LLM-MLLM collaboration to correct given captions.<n>Our proposed method significantly enhances the factual accuracy of captions, even improving those generated by GPT-4V.
  arXiv  Detail & Related papers  (2024-12-20T01:37:22Z)
• LLM-SEM: A Sentiment-Based Student Engagement Metric Using LLMS for E-Learning Platforms [0.0]
  LLM-SEM (Language Model-Based Student Engagement Metric) is a novel approach that leverages video metadata and sentiment analysis of student comments to measure engagement. We generate high-quality sentiment predictions to mitigate text fuzziness and normalize key features such as views and likes. Our holistic method combines comprehensive metadata with sentiment polarity scores to gauge engagement at both the course and lesson levels.
  arXiv  Detail & Related papers  (2024-12-18T12:01:53Z)
• Beyond Binary: Towards Fine-Grained LLM-Generated Text Detection via Role Recognition and Involvement Measurement [51.601916604301685]
  Large language models (LLMs) generate content that can undermine trust in online discourse. Current methods often focus on binary classification, failing to address the complexities of real-world scenarios like human-AI collaboration. To move beyond binary classification and address these challenges, we propose a new paradigm for detecting LLM-generated content.
  arXiv  Detail & Related papers  (2024-10-18T08:14:10Z)
• Unveiling Large Language Models Generated Texts: A Multi-Level Fine-Grained Detection Framework [9.976099891796784]
  Large language models (LLMs) have transformed human writing by enhancing grammar correction, content expansion, and stylistic refinement. Existing detection methods, which mainly rely on single-feature analysis and binary classification, often fail to effectively identify LLM-generated text in academic contexts. We propose a novel Multi-level Fine-grained Detection framework that detects LLM-generated text by integrating low-level structural, high-level semantic, and deep-level linguistic features.
  arXiv  Detail & Related papers  (2024-10-18T07:25:00Z)
• RA-BLIP: Multimodal Adaptive Retrieval-Augmented Bootstrapping Language-Image Pre-training [55.54020926284334]
  Multimodal Large Language Models (MLLMs) have recently received substantial interest, which shows their emerging potential as general-purpose models for various vision-language tasks. Retrieval augmentation techniques have proven to be effective plugins for both LLMs and MLLMs. In this study, we propose multimodal adaptive Retrieval-Augmented Bootstrapping Language-Image Pre-training (RA-BLIP), a novel retrieval-augmented framework for various MLLMs.
  arXiv  Detail & Related papers  (2024-10-18T03:45:19Z)
• Evaluating Linguistic Capabilities of Multimodal LLMs in the Lens of Few-Shot Learning [15.919493497867567]
  This study aims to evaluate the performance of Multimodal Large Language Models (MLLMs) on the VALSE benchmark. We conducted a comprehensive assessment of state-of-the-art MLLMs, varying in model size and pretraining datasets.
  arXiv  Detail & Related papers  (2024-07-17T11:26:47Z)
• Improving Visual Commonsense in Language Models via Multiple Image Generation [41.565399860320966]
  Existing large language models (LLMs) are primarily trained using textual data only. Visual Language Models, which excel at visually-oriented tasks, often fail at non-visual tasks such as basic commonsense reasoning. This divergence highlights a critical challenge - the integration of robust visual understanding with foundational text-based language reasoning.
  arXiv  Detail & Related papers  (2024-06-19T15:17:10Z)
• Towards Reliable Detection of LLM-Generated Texts: A Comprehensive Evaluation Framework with CUDRT [9.682499180341273]
  Large language models (LLMs) have significantly advanced text generation, but the human-like quality of their outputs presents major challenges.<n>We propose CUDRT, a comprehensive evaluation framework and bilingual benchmark in Chinese and English.<n>This framework supports scalable, reproducible experiments and enables analysis of how operational diversity, multilingual training sets, and LLM architectures influence detection performance.
  arXiv  Detail & Related papers  (2024-06-13T12:43:40Z)
• Debiasing Multimodal Large Language Models [61.6896704217147]
  Large Vision-Language Models (LVLMs) have become indispensable tools in computer vision and natural language processing. Our investigation reveals a noteworthy bias in the generated content, where the output is primarily influenced by the underlying Large Language Models (LLMs) prior to the input image. To rectify these biases and redirect the model's focus toward vision information, we introduce two simple, training-free strategies.
  arXiv  Detail & Related papers  (2024-03-08T12:35:07Z)
• Incorporating Visual Experts to Resolve the Information Loss in Multimodal Large Language Models [121.83413400686139]
  This paper proposes to improve the visual perception ability of MLLMs through a mixture-of-experts knowledge enhancement mechanism. We introduce a novel method that incorporates multi-task encoders and visual tools into the existing MLLMs training and inference pipeline.
  arXiv  Detail & Related papers  (2024-01-06T02:02:34Z)
• A Survey on Detection of LLMs-Generated Content [97.87912800179531]
  The ability to detect LLMs-generated content has become of paramount importance. We aim to provide a detailed overview of existing detection strategies and benchmarks. We also posit the necessity for a multi-faceted approach to defend against various attacks.
  arXiv  Detail & Related papers  (2023-10-24T09:10:26Z)
• Sieve: Multimodal Dataset Pruning Using Image Captioning Models [11.362835828985494]
  Vision-Language Models (VLMs) are pretrained on large, diverse, and noisy web-crawled datasets. We argue that this approach suffers from multiple limitations including false positives and negatives due to CLIP's pretraining on noisy labels. We propose a pruning signal, Sieve, that employs synthetic captions generated by image-captioning models pretrained on small, diverse, and well-aligned image-text pairs.
  arXiv  Detail & Related papers  (2023-10-03T14:53:53Z)
• Sight Beyond Text: Multi-Modal Training Enhances LLMs in Truthfulness and Ethics [32.123919380959485]
  Multi-modal large language models (MLLMs) are trained based on large language models (LLM) While they excel in multi-modal tasks, the pure NLP abilities of MLLMs are often underestimated and left untested. We show that visual instruction tuning, a prevailing strategy for transitioning LLMs into MLLMs, unexpectedly and interestingly helps models attain both improved truthfulness and ethical alignment.
  arXiv  Detail & Related papers  (2023-09-13T17:57:21Z)
• Sentiment Analysis in the Era of Large Language Models: A Reality Check [69.97942065617664]
  This paper investigates the capabilities of large language models (LLMs) in performing various sentiment analysis tasks. We evaluate performance across 13 tasks on 26 datasets and compare the results against small language models (SLMs) trained on domain-specific datasets.
  arXiv  Detail & Related papers  (2023-05-24T10:45:25Z)
• UATVR: Uncertainty-Adaptive Text-Video Retrieval [90.8952122146241]
  A common practice is to transfer text-video pairs to the same embedding space and craft cross-modal interactions with certain entities. We propose an Uncertainty-language Text-Video Retrieval approach, termed UATVR, which models each look-up as a distribution matching procedure.
  arXiv  Detail & Related papers  (2023-01-16T08:43:17Z)
• Few-shot Instruction Prompts for Pretrained Language Models to Detect Social Biases [55.45617404586874]
  We propose a few-shot instruction-based method for prompting pre-trained language models (LMs) We show that large LMs can detect different types of fine-grained biases with similar and sometimes superior accuracy to fine-tuned models.
  arXiv  Detail & Related papers  (2021-12-15T04:19:52Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.