{"paper":{"title":"HyperLLaVA: Dynamic Visual and Language Expert Tuning for Multimodal Large Language Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL","cs.CV"],"primary_cat":"cs.AI","authors_text":"Fangxun Shu, Hao Jiang, Haoyuan Li, Hao Zhou, He Wanggui, Jiang Liu, Juncheng Li, Lei Zhang, Siliang Tang, Tianwei Lin, Wenqiao Zhang, Yueting Zhuang, Zheqi Lv","submitted_at":"2024-03-20T09:42:43Z","abstract_excerpt":"Recent advancements indicate that scaling up Multimodal Large Language Models (MLLMs) effectively enhances performance on downstream multimodal tasks. The prevailing MLLM paradigm, \\emph{e.g.}, LLaVA, transforms visual features into text-like tokens using a \\emph{static} vision-language mapper, thereby enabling \\emph{static} LLMs to develop the capability to comprehend visual information through visual instruction tuning. Although promising, the \\emph{static} tuning strategy~\\footnote{The static tuning refers to the trained model with static parameters.} that shares the same parameters may con"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.13447","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2403.13447/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}