{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/dept-decomposed-prompt-tuning-for-parameter","title":"DePT: Decomposed Prompt Tuning for Parameter-Efficient Fine-tuning","arxiv_id":"2309.05173","date":"2023-09-11","proceeding":null,"authors":["Zhengxiang Shi","Aldo Lipani"],"abstract":"Prompt tuning (PT), where a small amount of trainable soft (continuous) prompt vectors is affixed to the input of language models (LM), has shown promising results across various tasks and models for parameter-efficient fine-tuning (PEFT). PT stands out from other PEFT approaches because it maintains competitive performance with fewer trainable parameters and does not drastically scale up its parameters as the model size expands. However, PT introduces additional soft prompt tokens, leading to longer input sequences, which significantly impacts training and inference time and memory usage due to the Transformer's quadratic complexity. Particularly concerning for Large Language Models (LLMs) that face heavy daily querying. To address this issue, we propose Decomposed Prompt Tuning (DePT), which decomposes the soft prompt into a shorter soft prompt and a pair of low-rank matrices that are then optimised with two different learning rates. This allows DePT to achieve better performance while saving substantial memory and time costs compared to vanilla PT and its variants, without changing trainable parameter sizes. Through extensive experiments on 23 natural language processing (NLP) and vision-language (VL) tasks, we demonstrate that DePT outperforms state-of-the-art PEFT approaches, including the full fine-tuning baseline, in some scenarios. Additionally, we empirically show that DEPT grows more efficient as the model size increases. Our further study reveals that DePT integrates seamlessly with parameter-efficient transfer learning in the few-shot learning setting and highlights its adaptability to various model architectures and sizes.","url_abs":"https://arxiv.org/abs/2309.05173v5","url_pdf":"https://arxiv.org/pdf/2309.05173v5.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":"dept-decomposed-prompt-tuning-for-parameter","repo_url":"https://github.com/zhengxiangshi/dept","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"dept-decomposed-prompt-tuning-for-parameter","repo_url":"https://github.com/zhengxiangshi/powerfulpromptft","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"few-shot-learning","task_name":"Few-Shot Learning"},{"task_slug":"transfer-learning","task_name":"Transfer Learning"},{"task_slug":"parameter-efficient-fine-tuning","task_name":"parameter-efficient fine-tuning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=2309.05173","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.05173"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+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. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/zhengxiangshi/dept","reach":null},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/zhengxiangshi/powerfulpromptft","reach":{"status":"ok","spdx":"MIT"}}],"summary":{"ran":5,"ran_draft_wrong":3,"unverified":1},"by_repo_kind":{"official":{"samples":1,"ran":0,"repositories":1},"listed":{"samples":8,"ran":8,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"5f5e5de530a5a8b4","entry":"count_parameters","repo":"zhengxiangshi/powerfulpromptft","repo_kind":"listed","path":"src/model.py","file_url":"https://github.com/zhengxiangshi/powerfulpromptft/blob/HEAD/src/model.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"5f5e5de530a5a8b4"}},{"code_sha256_prefix":"ff1816cf44827a08","entry":"dart_template","repo":"zhengxiangshi/powerfulpromptft","repo_kind":"listed","path":"src/convert_to_pretrain_pcp.py","file_url":"https://github.com/zhengxiangshi/powerfulpromptft/blob/HEAD/src/convert_to_pretrain_pcp.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"ff1816cf44827a08"}},{"code_sha256_prefix":"d9f08b1debac0613","entry":"dart_ts_template","repo":"zhengxiangshi/powerfulpromptft","repo_kind":"listed","path":"src/convert_to_pretrain_pcp.py","file_url":"https://github.com/zhengxiangshi/powerfulpromptft/blob/HEAD/src/convert_to_pretrain_pcp.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"d9f08b1debac0613"}},{"code_sha256_prefix":"126ae8eeb63d59bf","entry":"get_label","repo":"zhengxiangshi/powerfulpromptft","repo_kind":"listed","path":"src/convert_to_pretrain_tapt_1.py","file_url":"https://github.com/zhengxiangshi/powerfulpromptft/blob/HEAD/src/convert_to_pretrain_tapt_1.py","link_basis":"plan_row","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"126ae8eeb63d59bf"}},{"code_sha256_prefix":"8e27652a29c8adcc","entry":"load_datasets","repo":"zhengxiangshi/powerfulpromptft","repo_kind":"listed","path":"src/convert_to_pretrain_tapt_1.py","file_url":"https://github.com/zhengxiangshi/powerfulpromptft/blob/HEAD/src/convert_to_pretrain_tapt_1.py","link_basis":"plan_row","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"8e27652a29c8adcc"}},{"code_sha256_prefix":"efd1b7718b950470","entry":"split_header","repo":"zhengxiangshi/powerfulpromptft","repo_kind":"listed","path":"src/convert_to_pretrain_tapt_1.py","file_url":"https://github.com/zhengxiangshi/powerfulpromptft/blob/HEAD/src/convert_to_pretrain_tapt_1.py","link_basis":"plan_row","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"efd1b7718b950470"}},{"code_sha256_prefix":"edb11b8546838e1f","entry":"ssl_template","repo":"zhengxiangshi/powerfulpromptft","repo_kind":"listed","path":"src/convert_to_pretrain_pcp.py","file_url":"https://github.com/zhengxiangshi/powerfulpromptft/blob/HEAD/src/convert_to_pretrain_pcp.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"edb11b8546838e1f"}},{"code_sha256_prefix":"b62076c5483738dc","entry":"text_classification_metrics","repo":"zhengxiangshi/powerfulpromptft","repo_kind":"listed","path":"src/processors.py","file_url":"https://github.com/zhengxiangshi/powerfulpromptft/blob/HEAD/src/processors.py","link_basis":"harvester_set","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"b62076c5483738dc"}},{"code_sha256_prefix":"55df58071d2c9185","entry":"PromptEmbeddingLoRA","repo":"zhengxiangshi/dept","repo_kind":"official","path":"peft/tuners/prompt_tuning_lora.py","file_url":"https://github.com/zhengxiangshi/dept/blob/HEAD/peft/tuners/prompt_tuning_lora.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"55df58071d2c9185"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}