From Individuals to Interactions: Benchmarking Gender Bias in Multimodal Large Language Models from the Lens of Social Relationship | Paper Citation Record

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This paper cites Phi-4-Mini Technical Report: Compact yet Powerful Multimodal Language Models via Mixture-of-LoRAs.

From Individuals to Interactions: Benchmarking Gender Bias in Multimodal Large Language Models from the Lens of Social Relationship Phi-4-Mini Technical Report: Compact yet Powerful Multimodal Language Models via Mixture-of-LoRAs

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From Individuals to Interactions: Benchmarking Gender Bias in Multimodal Large Language Models from the Lens of Social Relationship GPT-4 Technical Report

Reference 2

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From Individuals to Interactions: Benchmarking Gender Bias in Multimodal Large Language Models from the Lens of Social Relationship Qwen2.5-VL Technical Report

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This paper cites A multidimensional analysis of social biases in vision transformers.

From Individuals to Interactions: Benchmarking Gender Bias in Multimodal Large Language Models from the Lens of Social Relationship A multidimensional analysis of social biases in vision transformers

Reference 4

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This paper cites Benchlmm: Benchmarking cross-style visual capability of large multimodal models.

From Individuals to Interactions: Benchmarking Gender Bias in Multimodal Large Language Models from the Lens of Social Relationship Benchlmm: Benchmarking cross-style visual capability of large multimodal models

Reference 5

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This paper cites From structured prompts to open narra- tives: Measuring gender bias in llms through open-ended storytelling.

From Individuals to Interactions: Benchmarking Gender Bias in Multimodal Large Language Models from the Lens of Social Relationship From structured prompts to open narra- tives: Measuring gender bias in llms through open-ended storytelling

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This paper cites Janus-Pro: Unified Multimodal Understanding and Generation with Data and Model Scaling.

From Individuals to Interactions: Benchmarking Gender Bias in Multimodal Large Language Models from the Lens of Social Relationship Janus-Pro: Unified Multimodal Understanding and Generation with Data and Model Scaling

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This paper cites PhysBench: Benchmarking and Enhancing Vision-Language Models for Physical World Understanding.

From Individuals to Interactions: Benchmarking Gender Bias in Multimodal Large Language Models from the Lens of Social Relationship PhysBench: Benchmarking and Enhancing Vision-Language Models for Physical World Understanding

Reference 8

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This paper cites The four elementary forms of sociality: framework for a unified theory of social relations.

From Individuals to Interactions: Benchmarking Gender Bias in Multimodal Large Language Models from the Lens of Social Relationship The four elementary forms of sociality: framework for a unified theory of social relations

Reference 9

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From Individuals to Interactions: Benchmarking Gender Bias in Multimodal Large Language Models from the Lens of Social Relationship A model of (often mixed) stereotype content: Competence and warmth respectively follow from perceived status and competition

Reference 10

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This paper cites Examining Gender and Racial Bias in Large Vision-Language Models Using a Novel Dataset of Parallel Images.

From Individuals to Interactions: Benchmarking Gender Bias in Multimodal Large Language Models from the Lens of Social Relationship Examining Gender and Racial Bias in Large Vision-Language Models Using a Novel Dataset of Parallel Images

Reference 11

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This paper cites Revealing and Reducing Gender Biases in Vision and Language Assistants (VLAs).

From Individuals to Interactions: Benchmarking Gender Bias in Multimodal Large Language Models from the Lens of Social Relationship Revealing and Reducing Gender Biases in Vision and Language Assistants (VLAs)

Reference 12

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This paper cites A large scale analysis of gender biases in text-to-image generative models.

From Individuals to Interactions: Benchmarking Gender Bias in Multimodal Large Language Models from the Lens of Social Relationship A large scale analysis of gender biases in text-to-image generative models

Reference 13

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This paper cites Gemini — deepmind.google.

From Individuals to Interactions: Benchmarking Gender Bias in Multimodal Large Language Models from the Lens of Social Relationship Gemini — deepmind.google

Reference 14

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This paper cites Mllmguard: A multi-dimensional safety evaluation suite for multimodal large language models.

From Individuals to Interactions: Benchmarking Gender Bias in Multimodal Large Language Models from the Lens of Social Relationship Mllmguard: A multi-dimensional safety evaluation suite for multimodal large language models

Reference 15

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This paper cites Visogender: A dataset for benchmarking gender bias in image-text pronoun resolution.

From Individuals to Interactions: Benchmarking Gender Bias in Multimodal Large Language Models from the Lens of Social Relationship Visogender: A dataset for benchmarking gender bias in image-text pronoun resolution

Reference 16

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This paper cites Uncovering Bias in Large Vision-Language Models with Counterfactuals.

From Individuals to Interactions: Benchmarking Gender Bias in Multimodal Large Language Models from the Lens of Social Relationship Uncovering Bias in Large Vision-Language Models with Counterfactuals

Reference 17

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This paper cites Socialcounterfactuals: Probing and mitigating intersectional social biases in vision-language models with counterfactual examples.

From Individuals to Interactions: Benchmarking Gender Bias in Multimodal Large Language Models from the Lens of Social Relationship Socialcounterfactuals: Probing and mitigating intersectional social biases in vision-language models with counterfactual examples

Reference 18

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This paper cites VisBias: Measuring Explicit and Implicit Social Biases in Vision Language Models.

From Individuals to Interactions: Benchmarking Gender Bias in Multimodal Large Language Models from the Lens of Social Relationship VisBias: Measuring Explicit and Implicit Social Biases in Vision Language Models

Reference 19

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From Individuals to Interactions: Benchmarking Gender Bias in Multimodal Large Language Models from the Lens of Social Relationship GPT-4o System Card

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From Individuals to Interactions: Benchmarking Gender Bias in Multimodal Large Language Models from the Lens of Social Relationship Gender Bias in LLM-generated Interview Responses

Reference 21

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From Individuals to Interactions: Benchmarking Gender Bias in Multimodal Large Language Models from the Lens of Social Relationship Natural language understanding and inference with mllm in visual question answering: A survey

Reference 22

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This paper cites Gender Bias in Decision-Making with Large Language Models: A Study of Relationship Conflicts.

From Individuals to Interactions: Benchmarking Gender Bias in Multimodal Large Language Models from the Lens of Social Relationship Gender Bias in Decision-Making with Large Language Models: A Study of Relationship Conflicts

Reference 23

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This paper cites Multimodal Composite Association Score: Measuring Gender Bias in Generative Multimodal Models.

From Individuals to Interactions: Benchmarking Gender Bias in Multimodal Large Language Models from the Lens of Social Relationship Multimodal Composite Association Score: Measuring Gender Bias in Generative Multimodal Models

Reference 24

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From Individuals to Interactions: Benchmarking Gender Bias in Multimodal Large Language Models from the Lens of Social Relationship Nrc emotion lexicon

Reference 25

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From Individuals to Interactions: Benchmarking Gender Bias in Multimodal Large Language Models from the Lens of Social Relationship Social biases through the text-to-image generation lens

Reference 26

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From Individuals to Interactions: Benchmarking Gender Bias in Multimodal Large Language Models from the Lens of Social Relationship Sb-bench: Stereotype bias benchmark for large multimodal models

Reference 27

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From Individuals to Interactions: Benchmarking Gender Bias in Multimodal Large Language Models from the Lens of Social Relationship Comprehensive stereotype content dictionaries using a semi-automated method

Reference 28

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From Individuals to Interactions: Benchmarking Gender Bias in Multimodal Large Language Models from the Lens of Social Relationship BBQ: A Hand-Built Bias Benchmark for Question Answering

Reference 29

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From Individuals to Interactions: Benchmarking Gender Bias in Multimodal Large Language Models from the Lens of Social Relationship SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis

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From Individuals to Interactions: Benchmarking Gender Bias in Multimodal Large Language Models from the Lens of Social Relationship Gender Biases in Automatic Evaluation Metrics for Image Captioning

Reference 31

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From Individuals to Interactions: Benchmarking Gender Bias in Multimodal Large Language Models from the Lens of Social Relationship Learning transferable visual models from natural language supervision

Reference 32

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This paper cites BiasDora: Exploring Hidden Biased Associations in Vision-Language Models.

From Individuals to Interactions: Benchmarking Gender Bias in Multimodal Large Language Models from the Lens of Social Relationship BiasDora: Exploring Hidden Biased Associations in Vision-Language Models

Reference 33

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This paper cites A multi-dimensional study on bias in vision-language models.

From Individuals to Interactions: Benchmarking Gender Bias in Multimodal Large Language Models from the Lens of Social Relationship A multi-dimensional study on bias in vision-language models

Reference 34

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This paper cites Worst of Both Worlds: Biases Compound in Pre-trained Vision-and-Language Models.

From Individuals to Interactions: Benchmarking Gender Bias in Multimodal Large Language Models from the Lens of Social Relationship Worst of Both Worlds: Biases Compound in Pre-trained Vision-and-Language Models

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From Individuals to Interactions: Benchmarking Gender Bias in Multimodal Large Language Models from the Lens of Social Relationship "Kelly is a Warm Person, Joseph is a Role Model": Gender Biases in LLM-Generated Reference Letters

Reference 36

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From Individuals to Interactions: Benchmarking Gender Bias in Multimodal Large Language Models from the Lens of Social Relationship Survey of Bias In Text-to-Image Generation: Definition, Evaluation, and Mitigation

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This paper cites VLBiasBench: A Comprehensive Benchmark for Evaluating Bias in Large Vision-Language Model.

From Individuals to Interactions: Benchmarking Gender Bias in Multimodal Large Language Models from the Lens of Social Relationship VLBiasBench: A Comprehensive Benchmark for Evaluating Bias in Large Vision-Language Model

Reference 38

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From Individuals to Interactions: Benchmarking Gender Bias in Multimodal Large Language Models from the Lens of Social Relationship Genderbias-vl: Benchmarking gender bias in vision language models via counterfactual probing

Reference 39

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This paper cites Benchmarking Large Multimodal Models against Common Corruptions.

From Individuals to Interactions: Benchmarking Gender Bias in Multimodal Large Language Models from the Lens of Social Relationship Benchmarking Large Multimodal Models against Common Corruptions

Reference 40

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From Individuals to Interactions: Benchmarking Gender Bias in Multimodal Large Language Models from the Lens of Social Relationship Understanding and evaluating racial biases in image captioning

Reference 41

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This paper cites Gender Bias in Coreference Resolution: Evaluation and Debiasing Methods.

From Individuals to Interactions: Benchmarking Gender Bias in Multimodal Large Language Models from the Lens of Social Relationship Gender Bias in Coreference Resolution: Evaluation and Debiasing Methods

Reference 42

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From Individuals to Interactions: Benchmarking Gender Bias in Multimodal Large Language Models from the Lens of Social Relationship Vlstereoset: A study of stereotypical bias in pre-trained vision- language models

Reference 43

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From Individuals to Interactions: Benchmarking Gender Bias in Multimodal Large Language Models from the Lens of Social Relationship male” and “female

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This paper cites [High status question].

From Individuals to Interactions: Benchmarking Gender Bias in Multimodal Large Language Models from the Lens of Social Relationship [High status question]

Reference 45

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This paper cites Use adjective words as much as possible and separate them with a comma.

From Individuals to Interactions: Benchmarking Gender Bias in Multimodal Large Language Models from the Lens of Social Relationship Use adjective words as much as possible and separate them with a comma

Reference 46

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From Individuals to Interactions: Benchmarking Gender Bias in Multimodal Large Language Models from the Lens of Social Relationship Do not make any assumptions

Reference 47

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This paper cites Answer to Task1.a.

From Individuals to Interactions: Benchmarking Gender Bias in Multimodal Large Language Models from the Lens of Social Relationship Answer to Task1.a

Reference 48

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