# Instance-aware Prompt Learning for Language Understanding and Generation

arXiv: https://arxiv.org/abs/2201.07126 · HTML: https://mlsanity.com/paper/2201.07126/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 48 references that are in the map.
- First posted on arXiv 18 Jan 2022.

## Builds on (most cited first, 29 of 29)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [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)
- [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://mlsanity.com/paper/1909.11942/) (2019)
- [BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions](https://mlsanity.com/paper/1905.10044/) (2019)
- [CIDEr: Consensus-based Image Description Evaluation](https://mlsanity.com/paper/1411.5726/) (2014)
- [SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems](https://mlsanity.com/paper/1905.00537/) (2019)
- [Making Pre-trained Language Models Better Few-shot Learners](https://mlsanity.com/paper/2012.15723/) (2020)
- [How Can We Know What Language Models Know?](https://mlsanity.com/paper/1911.12543/) (2019)
- [Exploiting Cloze Questions for Few Shot Text Classification and Natural Language Inference](https://mlsanity.com/paper/2001.07676/) (2020)
- [GPT Understands, Too](https://mlsanity.com/paper/2103.10385/) (2021)
- [P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks](https://mlsanity.com/paper/2110.07602/) (2021)
- [It's Not Just Size That Matters: Small Language Models Are Also Few-Shot Learners](https://mlsanity.com/paper/2009.07118/) (2020)
- [SAMSum Corpus: A Human-annotated Dialogue Dataset for Abstractive Summarization](https://mlsanity.com/paper/1911.12237/) (2019)
- [WiC: the Word-in-Context Dataset for Evaluating Context-Sensitive Meaning Representations](https://mlsanity.com/paper/1808.09121/) (2018)
- [Factual Probing Is [MASK]: Learning vs. Learning to Recall](https://mlsanity.com/paper/2104.05240/) (2021)
- [The E2E Dataset: New Challenges For End-to-End Generation](https://mlsanity.com/paper/1706.09254/) (2017)
- [AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts](https://mlsanity.com/paper/2010.15980/) (2020)
- [A Simple Method for Commonsense Reasoning](https://mlsanity.com/paper/1806.02847/) (2018)
- [WARP: Word-level Adversarial ReProgramming](https://mlsanity.com/paper/2101.00121/) (2021)
- [ReCoRD: Bridging the Gap between Human and Machine Commonsense Reading Comprehension](https://mlsanity.com/paper/1810.12885/) (2018)
- [DialogSum: A Real-Life Scenario Dialogue Summarization Dataset](https://mlsanity.com/paper/2105.06762/) (2021)
- [DART: Open-Domain Structured Data Record to Text Generation](https://mlsanity.com/paper/2007.02871/) (2020)
- [Improving and Simplifying Pattern Exploiting Training](https://mlsanity.com/paper/2103.11955/) (2021)
- [RobBERT: a Dutch RoBERTa-based Language Model](https://mlsanity.com/paper/2001.06286/) (2020)
- [Response Generation with Context-Aware Prompt Learning](https://mlsanity.com/paper/2111.02643/) (2021)

## Built on by (most cited first, 18 of 18)

- [Domain Specialization as the Key to Make Large Language Models Disruptive: A Comprehensive Survey](https://mlsanity.com/paper/2305.18703/) (2023)
- [Cobra: Extending Mamba to Multi-Modal Large Language Model for Efficient Inference](https://mlsanity.com/paper/2403.14520/) (2024)
- [Confidence Matters: Revisiting Intrinsic Self-Correction Capabilities of Large Language Models](https://mlsanity.com/paper/2402.12563/) (2024)
- [Standing on the Shoulders of Giant Frozen Language Models](https://mlsanity.com/paper/2204.10019/) (2022)
- [Late Prompt Tuning: A Late Prompt Could Be Better Than Many Prompts](https://mlsanity.com/paper/2210.11292/) (2022)
- [UnifieR: A Unified Retriever for Large-Scale Retrieval](https://mlsanity.com/paper/2205.11194/) (2022)
- [LingYi: Medical Conversational Question Answering System based on Multi-modal Knowledge Graphs](https://mlsanity.com/paper/2204.09220/) (2022)
- [Relation Extraction as Open-book Examination: Retrieval-enhanced Prompt Tuning](https://mlsanity.com/paper/2205.02355/) (2022)
- [Prompting through Prototype: A Prototype-based Prompt Learning on Pretrained Vision-Language Models](https://mlsanity.com/paper/2210.10841/) (2022)
- [Derivative-Free Optimization for Low-Rank Adaptation in Large Language Models](https://mlsanity.com/paper/2403.01754/) (2024)
- [Vector-Quantized Input-Contextualized Soft Prompts for Natural Language Understanding](https://mlsanity.com/paper/2205.11024/) (2022)
- [Exploring Lottery Prompts for Pre-trained Language Models](https://mlsanity.com/paper/2305.19500/) (2023)
- [Attribute Controlled Dialogue Prompting](https://mlsanity.com/paper/2307.05228/) (2023)
- [Progressive Learning with Visual Prompt Tuning for Variable-Rate Image Compression](https://mlsanity.com/paper/2311.13846/) (2023)
- [Enhancing Low-Resource Relation Representations through Multi-View Decoupling](https://mlsanity.com/paper/2312.17267/) (2023)
- [GPS: General Per-Sample Prompter](https://mlsanity.com/paper/2511.21714/) (2025)
- [DialCLIP: Empowering CLIP as Multi-Modal Dialog Retriever](https://mlsanity.com/paper/2401.01076/) (2024)
- [Improving Prompt Tuning with Learned Prompting Layers](https://mlsanity.com/paper/2310.20127/) (2023)

## Abstract (arXiv)

Recently, prompt learning has become a new paradigm to utilize pre-trained language models (PLMs) and achieves promising results in downstream tasks with a negligible increase of parameters. The current usage of discrete and continuous prompts assumes that the prompt is fixed for a specific task and all samples in the task share the same prompt. However, a task may contain quite diverse samples in which some are easy and others are difficult, and diverse prompts are desirable. In this paper, we propose an instance-aware prompt learning method that learns a different prompt for each instance. Specifically, we suppose that each learnable prompt token has a different contribution to different instances, and we learn the contribution by calculating the relevance score between an instance and each prompt token. The contribution weighted prompt would be instance aware. We apply our method to both unidirectional and bidirectional PLMs on both language understanding and generation tasks. Extensive experiments demonstrate that our method obtains considerable improvements compared to strong baselines. Especially, our method achieves the state-of-the-art on the SuperGLUE few-shot learning benchmark.