# Non-Intrusive Adaptation: Input-Centric Parameter-efficient Fine-Tuning for Versatile Multimodal Modeling

arXiv: https://arxiv.org/abs/2310.12100 · HTML: https://mlsanity.com/paper/2310.12100/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Placed in MLLM training and visual instruction tuning, one of 16 directions in Multimodal LLMs / vision-language models.
- Builds on 46 of its 46 references that are in the map.
- First posted on arXiv 18 Oct 2023.

## Placed in

- Multimodal LLMs / vision-language models / [MLLM training and visual instruction tuning](https://mlsanity.com/mllm/mllm-training-and-visual-instruction-tuning/)

## Builds on (most cited first, 30 of 46)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Visual Instruction Tuning](https://mlsanity.com/paper/2304.08485/) (2023)
- [BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models](https://mlsanity.com/paper/2301.12597/) (2023)
- [PaLM: Scaling Language Modeling with Pathways](https://mlsanity.com/paper/2204.02311/) (2022)
- [GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding](https://mlsanity.com/paper/1804.07461/) (2018)
- [Flamingo: a Visual Language Model for Few-Shot Learning](https://mlsanity.com/paper/2204.14198/) (2022)
- [Parameter-Efficient Transfer Learning for NLP](https://mlsanity.com/paper/1902.00751/) (2019)
- [Prefix-Tuning: Optimizing Continuous Prompts for Generation](https://mlsanity.com/paper/2101.00190/) (2021)
- [The Power of Scale for Parameter-Efficient Prompt Tuning](https://mlsanity.com/paper/2104.08691/) (2021)
- [Finetuned Language Models Are Zero-Shot Learners](https://mlsanity.com/paper/2109.01652/) (2021)
- [Scaling Instruction-Finetuned Language Models](https://mlsanity.com/paper/2210.11416/) (2022)
- [Self-Instruct: Aligning Language Models with Self-Generated Instructions](https://mlsanity.com/paper/2212.10560/) (2022)
- [VQA: Visual Question Answering](https://mlsanity.com/paper/1505.00468/) (2015)
- [CIDEr: Consensus-based Image Description Evaluation](https://mlsanity.com/paper/1411.5726/) (2014)
- [Towards VQA Models That Can Read](https://mlsanity.com/paper/1904.08920/) (2019)
- [Multitask Prompted Training Enables Zero-Shot Task Generalization](https://mlsanity.com/paper/2110.08207/) (2021)
- [DocVQA: A Dataset for VQA on Document Images](https://mlsanity.com/paper/2007.00398/) (2020)
- [PaLM 2 Technical Report](https://mlsanity.com/paper/2305.10403/) (2023)
- [OK-VQA: A Visual Question Answering Benchmark Requiring External Knowledge](https://mlsanity.com/paper/1906.00067/) (2019)
- [Adafactor: Adaptive Learning Rates with Sublinear Memory Cost](https://mlsanity.com/paper/1804.04235/) (2018)
- [AdapterFusion: Non-Destructive Task Composition for Transfer Learning](https://mlsanity.com/paper/2005.00247/) (2020)
- [LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention](https://mlsanity.com/paper/2303.16199/) (2023)
- [The Flan Collection: Designing Data and Methods for Effective Instruction Tuning](https://mlsanity.com/paper/2301.13688/) (2023)

## Built on by (most cited first, 20 of 20)

- [DoRA: Weight-Decomposed Low-Rank Adaptation](https://mlsanity.com/paper/2402.09353/) (2024)
- [Visual Program Distillation: Distilling Tools and Programmatic Reasoning into Vision-Language Models](https://mlsanity.com/paper/2312.03052/) (2023)
- [Omni-SMoLA: Boosting Generalist Multimodal Models with Soft Mixture of Low-rank Experts](https://mlsanity.com/paper/2312.00968/) (2023)
- [See Further for Parameter Efficient Fine-tuning by Standing on the Shoulders of Decomposition](https://mlsanity.com/paper/2407.05417/) (2024)
- [Modeling Collaborator: Enabling Subjective Vision Classification With Minimal Human Effort via LLM Tool-Use](https://mlsanity.com/paper/2403.02626/) (2024)
- [Potential of Multimodal Large Language Models for Data Mining of Medical Images and Free-text Reports](https://mlsanity.com/paper/2407.05758/) (2024)
- [RepLoRA: Reparameterizing Low-Rank Adaptation via the Perspective of Mixture of Experts](https://mlsanity.com/paper/2502.03044/) (2025)
- [PaCA: Partial Connection Adaptation for Efficient Fine-Tuning](https://mlsanity.com/paper/2503.01905/) (2025)
- [Maintaining Structural Integrity in Parameter Spaces for Parameter Efficient Fine-tuning](https://mlsanity.com/paper/2405.14739/) (2024)
- [FlexLoRA: Entropy-Guided Flexible Low-Rank Adaptation](https://mlsanity.com/paper/2601.22905/) (2026)
- [Task-Specific Directions: Definition, Exploration, and Utilization in Parameter Efficient Fine-Tuning](https://mlsanity.com/paper/2409.01035/) (2024)
- [LoCA: Location-Aware Cosine Adaptation for Parameter-Efficient Fine-Tuning](https://mlsanity.com/paper/2502.06820/) (2025)
- [LoRA-DA: Data-Aware Initialization for Low-Rank Adaptation via Asymptotic Analysis](https://mlsanity.com/paper/2510.24561/) (2025)
- [LoRA Is Slower Than You Think](https://mlsanity.com/paper/2507.08833/) (2025)
- [SBoRA: Low-Rank Adaptation with Regional Weight Updates](https://mlsanity.com/paper/2407.05413/) (2024)
- [ALLoRA: Adaptive Learning Rate Mitigates LoRA Fatal Flaws](https://mlsanity.com/paper/2410.09692/) (2024)
- [Multi-Modal Parameter-Efficient Fine-tuning via Graph Neural Network](https://mlsanity.com/paper/2408.00290/) (2024)
- [MoDA: Modulation Adapter for Fine-Grained Visual Grounding in Instructional MLLMs](https://mlsanity.com/paper/2506.01850/) (2025)
- [PEML: Parameter-efficient Multi-Task Learning with Optimized Continuous Prompts](https://mlsanity.com/paper/2605.14055/) (2026)
- [PGSB: Pretrained-Guided Shared Basis for LoRA Model Merging](https://mlsanity.com/paper/2610.14558/) (2026)

## Abstract (arXiv)

Large language models (LLMs) and vision language models (VLMs) demonstrate excellent performance on a wide range of tasks by scaling up parameter counts from O(10^9) to O(10^{12}) levels and further beyond. These large scales make it impossible to adapt and deploy fully specialized models given a task of interest. Parameter-efficient fine-tuning (PEFT) emerges as a promising direction to tackle the adaptation and serving challenges for such large models. We categorize PEFT techniques into two types: intrusive and non-intrusive. Intrusive PEFT techniques directly change a model's internal architecture. Though more flexible, they introduce significant complexities for training and serving. Non-intrusive PEFT techniques leave the internal architecture unchanged and only adapt model-external parameters, such as embeddings for input. In this work, we describe AdaLink as a non-intrusive PEFT technique that achieves competitive performance compared to SoTA intrusive PEFT (LoRA) and full model fine-tuning (FT) on various tasks. We evaluate using both text-only and multimodal tasks, with experiments that account for both parameter-count scaling and training regime (with and without instruction tuning).