{"paper":{"title":"MiniGPT-v2: large language model as a unified interface for vision-language multi-task learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"MiniGPT-v2 uses unique task identifiers to let one large language model handle many vision-language tasks at once.","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Deyao Zhu, Jun Chen, Mohamed Elhoseiny, Pengchuan Zhang, Raghuraman Krishnamoorthi, Vikas Chandra, Xiang Li, Xiaoqian Shen, Yunyang Xiong, Zechun Liu","submitted_at":"2023-10-14T03:22:07Z","abstract_excerpt":"Large language models have shown their remarkable capabilities as a general interface for various language-related applications. Motivated by this, we target to build a unified interface for completing many vision-language tasks including image description, visual question answering, and visual grounding, among others. The challenge is to use a single model for performing diverse vision-language tasks effectively with simple multi-modal instructions. Towards this objective, we introduce MiniGPT-v2, a model that can be treated as a unified interface for better handling various vision-language t"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"After the three-stage training, the experimental results show that MiniGPT-v2 achieves strong performance on many visual question-answering and visual grounding benchmarks compared to other vision-language generalist models.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That assigning unique identifiers to tasks will let the model distinguish instructions and learn each task more efficiently without task interference or negative transfer, an assumption stated in the abstract but not quantified or ablated in the provided text.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"MiniGPT-v2 adds unique task identifiers to a large language model so one system can perform image description, visual question answering, and visual grounding after three-stage training.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"MiniGPT-v2 uses unique task identifiers to let one large language model handle many vision-language tasks at once.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"942d4ae387d649388f26f422d4882d9071fff76ca32f602718d3addd6d7f8c68"},"source":{"id":"2310.09478","kind":"arxiv","version":3},"verdict":{"id":"dd4f490b-96ef-4b5d-8768-80ec649f88b6","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-16T07:08:36.173359Z","strongest_claim":"After the three-stage training, the experimental results show that MiniGPT-v2 achieves strong performance on many visual question-answering and visual grounding benchmarks compared to other vision-language generalist models.","one_line_summary":"MiniGPT-v2 adds unique task identifiers to a large language model so one system can perform image description, visual question answering, and visual grounding after three-stage training.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That assigning unique identifiers to tasks will let the model distinguish instructions and learn each task more efficiently without task interference or negative transfer, an assumption stated in the abstract but not quantified or ablated in the provided text.","pith_extraction_headline":"MiniGPT-v2 uses unique task identifiers to let one large language model handle many vision-language tasks at once."},"references":{"count":61,"sample":[{"doi":"","year":2023,"title":"Sharegpt. https://github.com/domeccleston/sharegpt, 2023","work_id":"2a05eed8-5153-4b1f-840f-43037b06c7f6","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2022,"title":"Flamingo: a visual language model for few-shot learning","work_id":"059e2edb-7251-4c10-907e-c021375c785d","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2023,"title":"Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond","work_id":"cbc2bb21-b6bb-46c0-80bf-107e195ffe10","ref_index":3,"cited_arxiv_id":"2308.12966","is_internal_anchor":true},{"doi":"","year":1901,"title":"Language models are few-shot learners","work_id":"04bc68bc-b7df-4ec1-8599-da037bd4f085","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2022,"title":"Visualgpt: Data-efficient adaptation of pretrained language models for image captioning","work_id":"8d98bae4-2a67-4404-8191-97af7dbf6737","ref_index":5,"cited_arxiv_id":"","is_internal_anchor":false}],"resolved_work":61,"snapshot_sha256":"6acc954c00213e2eb916f3db0c5681eecffd60b8f3720c07e4f2be5ba0719177","internal_anchors":22},"formal_canon":{"evidence_count":2,"snapshot_sha256":"af61d1cb9e70ab9be95d1c0baf8ddc77ea61b855c7005af4aefe98e3ca6ae7f9"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}