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Found 5 papers, 4 papers with code
VALOR-EVAL: Holistic Coverage and Faithfulness Evaluation of Large Vision-Language Models
To address these issues, we introduce a multi-dimensional benchmark covering objects, attributes, and relations, with challenging images selected based on associative biases.
From Pixels to Insights: A Survey on Automatic Chart Understanding in the Era of Large Foundation Models
This survey paper serves as a comprehensive resource for researchers and practitioners in the fields of natural language processing, computer vision, and data analysis, providing valuable insights and directions for future research in chart understanding leveraging large foundation models.
New Job, New Gender? Measuring the Social Bias in Image Generation Models
BiasPainter uses a diverse range of seed images of individuals and prompts the image generation models to edit these images using gender, race, and age-neutral queries.
AMRFact: Enhancing Summarization Factuality Evaluation with AMR-Driven Negative Samples Generation
Prior works on evaluating factual consistency of summarization often take the entailment-based approaches that first generate perturbed (factual inconsistent) summaries and then train a classifier on the generated data to detect the factually inconsistencies during testing time.
Abstractive Text Summarization
Natural Language Inference
+1
Gender Biases in Automatic Evaluation Metrics for Image Captioning
Model-based evaluation metrics (e. g., CLIPScore and GPTScore) have demonstrated decent correlations with human judgments in various language generation tasks.
