{"paper":{"title":"MoDULA: Mixture of Domain-Specific and Universal LoRA for Multi-Task Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Ben Chen, Dehong Gao, Guannan Zhang, Huangyu Dai, Libin Yang, Linbo Jin, Wang Zihan, Wen Jiang, Xiaoyan Cai, Yufei Ma, Zhuoran Ran, Zihan Liang","submitted_at":"2024-12-10T10:55:57Z","abstract_excerpt":"The growing demand for larger-scale models in the development of \\textbf{L}arge \\textbf{L}anguage \\textbf{M}odels (LLMs) poses challenges for efficient training within limited computational resources. Traditional fine-tuning methods often exhibit instability in multi-task learning and rely heavily on extensive training resources. Here, we propose MoDULA (\\textbf{M}ixture \\textbf{o}f \\textbf{D}omain-Specific and \\textbf{U}niversal \\textbf{L}oR\\textbf{A}), a novel \\textbf{P}arameter \\textbf{E}fficient \\textbf{F}ine-\\textbf{T}uning (PEFT) \\textbf{M}ixture-\\textbf{o}f-\\textbf{E}xpert (MoE) paradig"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.07405","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/2412.07405/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"}