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To obtain detailed insights into the process of generating natural language explanations for VQA, we introduce the large-scale CLEVR-X dataset that extends the CLEVR dataset with natural language explanations. For each image-question pair in the CLEVR dataset, CLEVR-X contains multiple structured textual explanations which are derived from the original scene graphs. By construction, the CLEVR-X explanations are correct and describe the reasoning and visual information that is necessary to answer a given question. We conducted a user study to confirm that the ground-truth explanations in our proposed dataset are indeed complete and relevant. We present baseline results for generating natural language explanations in the context of VQA using two state-of-the-art frameworks on the CLEVR-X dataset. Furthermore, we provide a detailed analysis of the explanation generation quality for different question and answer types. Additionally, we study the influence of using different numbers of ground-truth explanations on the convergence of natural language generation (NLG) metrics. The CLEVR-X dataset is publicly available at \\url{https://explainableml.github.io/CLEVR-X/}.","url_abs":"https://arxiv.org/abs/2204.02380v1","url_pdf":"https://arxiv.org/pdf/2204.02380v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"clevr-x-a-visual-reasoning-dataset-for","repo_url":"https://github.com/explainableml/clevr-x","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"BSD-3-Clause"}}],"tasks":[{"task_slug":"explanation-generation","task_name":"Explanation Generation"},{"task_slug":"question-answering","task_name":"Question Answering"},{"task_slug":"text-generation","task_name":"Text Generation"},{"task_slug":"visual-question-answering-1","task_name":"Visual Question Answering"},{"task_slug":"visual-question-answering","task_name":"Visual Question Answering (VQA)"},{"task_slug":"visual-reasoning","task_name":"Visual Reasoning"}],"methods":[],"datasets_introduced":[{"slug":"clevr-x","name":"CLEVR-X","full_name":""}],"methods_introduced":[],"results":[{"leaderboard":"/sota/explanation-generation-on-clevr-x","task":"Explanation Generation","dataset":"CLEVR-X","model":"PJ-X","rank_in_archive_order":1,"of":2,"metrics":{"Acc":"63.0","B4":"87.4","C":"639.8","M":"58.9","RL":"93.4"},"uses_additional_data":false},{"leaderboard":"/sota/explanation-generation-on-clevr-x","task":"Explanation Generation","dataset":"CLEVR-X","model":"FM","rank_in_archive_order":2,"of":2,"metrics":{"Acc":"80.3","B4":"78.8","C":"566.8","M":"52.5","RL":"85.8"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/2204.02380","atlas_url":"https://app.syntology.ai/?focus=2204.02380","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2204.02380"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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