{"url":"/dataset/segsub","name":"SegSub","full_name":"SegSub: Evaluating Robustness to Knowledge Conflicts and Hallucinations in Vision-Language Models","description_markdown":"This research introduces \\segsub, a framework for applying targeted image perturbations to investigate VLM resilience against knowledge conflicts. Our analysis reveals distinct vulnerability patterns: while VLMs are robust to parametric conflicts (20% adherence rates), they exhibit significant weaknesses in identifying counterfactual conditions (<30% accuracy) and resolving source conflicts (<1% accuracy). Correlations between contextual richness and hallucination rate (r = -0.368, p = 0.003) reveal the kinds of images that are likely to cause hallucinations. Through targeted fine-tuning on our benchmark dataset, we demonstrate improvements in VLM knowledge conflict detection, establishing a foundation for developing hallucination-resilient multimodal systems in information-sensitive environments.","description_withheld":null,"homepage":"https://kilthub.cmu.edu/articles/dataset/SegSub_Enhancing_Robustness_in_Vision-Language_Models_with_Knowledge_Conflicts_and_Counterfactual_Image_Augmentation/28297076","introduced_date":"2025-02-19","introduced_date_note":null,"introduced_by":{"paper":"/paper/segsub-evaluating-robustness-to-knowledge","title":"SegSub: Evaluating Robustness to Knowledge Conflicts and Hallucinations in Vision-Language Models","first_author":"Peter Carragher","url":null},"license":null,"modalities":[],"tasks":[],"languages":[],"variants":["SegSub"],"data_loaders":[],"num_papers_in_archive":1,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}