@inproceedings{ravikumar-batista-navarro-2026-phaselora,
title = "{P}hase{L}o{RA}: Role-Specialised Low-Rank Adaptation through Prompt-Completion Phase Splitting",
author = "Ravikumar, Rishi and
Batista-Navarro, Riza",
editor = "Zhang, Yue and
Xu, Ruifeng and
Purwarianti, Ayu and
Rong, Tong and
Suzuki, Jun",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {IJCNLP}-{AACL} 2026",
month = nov,
year = "2026",
address = "Hengqin, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-aacl.95/",
pages = "1914--1925",
ISBN = "979-8-89176-444-6",
abstract = "In Low-Rank Adaptation (LoRA) fine-tuning of instruction-tuned LLMs, a single set of adapters is applied uniformly across all token positions, treating prompt and completion tokens identically. We question whether this is appropriate. The two phases serve semantically different roles: prompt tokens constitute the natural language input to be understood, while completion tokens constitute the task-specific output to be generated. Conflating the two under a single adapter set ignores a meaningful distinction that is intrinsic to the instruction-tuning paradigm. The phases are also computationally different: prompt tokens are conditioned on but never predicted, whereas completion tokens are generated autoregressively based on the preceding context. We introduce PhaseLoRA, an extension of LoRA that maintains independent adapter sets for the prompt and completion phases. Experiments across multiple benchmarks and a range of model scales and LoRA ranks show that PhaseLoRA consistently outperforms standard LoRA in low-capacity and low-rank settings. Beyond benchmark comparisons, we conduct mechanistic analysis of the learned adapter representations, structured ablations on warm-up initialisation and asymmetric rank allocation, and a quantitative error analysis characterising the inputs on which phase specialisation helps most."
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<abstract>In Low-Rank Adaptation (LoRA) fine-tuning of instruction-tuned LLMs, a single set of adapters is applied uniformly across all token positions, treating prompt and completion tokens identically. We question whether this is appropriate. The two phases serve semantically different roles: prompt tokens constitute the natural language input to be understood, while completion tokens constitute the task-specific output to be generated. Conflating the two under a single adapter set ignores a meaningful distinction that is intrinsic to the instruction-tuning paradigm. The phases are also computationally different: prompt tokens are conditioned on but never predicted, whereas completion tokens are generated autoregressively based on the preceding context. We introduce PhaseLoRA, an extension of LoRA that maintains independent adapter sets for the prompt and completion phases. Experiments across multiple benchmarks and a range of model scales and LoRA ranks show that PhaseLoRA consistently outperforms standard LoRA in low-capacity and low-rank settings. Beyond benchmark comparisons, we conduct mechanistic analysis of the learned adapter representations, structured ablations on warm-up initialisation and asymmetric rank allocation, and a quantitative error analysis characterising the inputs on which phase specialisation helps most.</abstract>
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%0 Conference Proceedings
%T PhaseLoRA: Role-Specialised Low-Rank Adaptation through Prompt-Completion Phase Splitting
%A Ravikumar, Rishi
%A Batista-Navarro, Riza
%Y Zhang, Yue
%Y Xu, Ruifeng
%Y Purwarianti, Ayu
%Y Rong, Tong
%Y Suzuki, Jun
%S Findings of the Association for Computational Linguistics: IJCNLP-AACL 2026
%D 2026
%8 November
%I Association for Computational Linguistics
%C Hengqin, China
%@ 979-8-89176-444-6
%F ravikumar-batista-navarro-2026-phaselora
%X In Low-Rank Adaptation (LoRA) fine-tuning of instruction-tuned LLMs, a single set of adapters is applied uniformly across all token positions, treating prompt and completion tokens identically. We question whether this is appropriate. The two phases serve semantically different roles: prompt tokens constitute the natural language input to be understood, while completion tokens constitute the task-specific output to be generated. Conflating the two under a single adapter set ignores a meaningful distinction that is intrinsic to the instruction-tuning paradigm. The phases are also computationally different: prompt tokens are conditioned on but never predicted, whereas completion tokens are generated autoregressively based on the preceding context. We introduce PhaseLoRA, an extension of LoRA that maintains independent adapter sets for the prompt and completion phases. Experiments across multiple benchmarks and a range of model scales and LoRA ranks show that PhaseLoRA consistently outperforms standard LoRA in low-capacity and low-rank settings. Beyond benchmark comparisons, we conduct mechanistic analysis of the learned adapter representations, structured ablations on warm-up initialisation and asymmetric rank allocation, and a quantitative error analysis characterising the inputs on which phase specialisation helps most.
%U https://aclanthology.org/2026.findings-aacl.95/
%P 1914-1925
Markdown (Informal)
[PhaseLoRA: Role-Specialised Low-Rank Adaptation through Prompt-Completion Phase Splitting](https://aclanthology.org/2026.findings-aacl.95/) (Ravikumar & Batista-Navarro, Findings 2026)
ACL