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Instance-Aware Prompt Learning for Language Understanding and Generation

https://doi.org/10.1145/3604613
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-3-642-45321-2_19openalex-snapshot
Benchmarking Applied Semantic Inference: The PASCAL Recognising Textual Entailment Challenges
inferredcites →https://doi.org/10.1162/tacl_a_00324openalex-snapshot
How Can We Know What Language Models Know?
inferredcites →https://doi.org/10.18148/sub/2019.v23i2.601openalex-snapshot
The CommitmentBank: Investigating projection in naturally occurring discourse
inferredcites →https://doi.org/10.18653/v1/2020.findings-emnlp.292openalex-snapshot
RobBERT: a Dutch RoBERTa-based Language Model
inferredcites →https://doi.org/10.18653/v1/2021.acl-long.295openalex-snapshot
Making Pre-trained Language Models Better Few-shot Learners
inferredcites →https://doi.org/10.18653/v1/2021.acl-long.353openalex-snapshot
Prefix-Tuning: Optimizing Continuous Prompts for Generation
inferredcites →https://doi.org/10.18653/v1/2021.acl-long.381openalex-snapshot
WARP: Word-level Adversarial ReProgramming
inferredcites →https://doi.org/10.18653/v1/2021.eacl-main.20openalex-snapshot
Exploiting Cloze-Questions for Few-Shot Text Classification and Natural Language Inference
inferredcites →https://doi.org/10.18653/v1/2021.emnlp-main.243openalex-snapshot
The Power of Scale for Parameter-Efficient Prompt Tuning
inferredcites →https://doi.org/10.18653/v1/2021.emnlp-main.407openalex-snapshot
Improving and Simplifying Pattern Exploiting Training
inferredcites →https://doi.org/10.18653/v1/2021.findings-acl.449openalex-snapshot
DialogSum: A Real-Life Scenario Dialogue Summarization Dataset
inferredcites →https://doi.org/10.18653/v1/2021.naacl-main.185openalex-snapshot
It’s Not Just Size That Matters: Small Language Models Are Also Few-Shot Learners
inferredcites →https://doi.org/10.18653/v1/2021.naacl-main.398openalex-snapshot
Factual Probing Is [MASK]: Learning vs. Learning to Recall
inferredcites →https://doi.org/10.18653/v1/d19-5409openalex-snapshot
SAMSum Corpus: A Human-annotated Dialogue Dataset for Abstractive Summarization
inferredcites →https://doi.org/10.18653/v1/n18-1023openalex-snapshot
Looking Beyond the Surface: A Challenge Set for Reading Comprehension over Multiple Sentences
inferredcites →https://doi.org/10.18653/v1/n19-1128openalex-snapshot
WiC: the Word-in-Context Dataset for Evaluating Context-Sensitive Meaning Representations
inferredcites →https://doi.org/10.18653/v1/n19-1300openalex-snapshot
BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions
inferredcites →https://doi.org/10.18653/v1/w17-3518openalex-snapshot
The WebNLG Challenge: Generating Text from RDF Data
inferredcites →https://doi.org/10.4230/lipics.cosit.2022.18openalex-snapshot
AI-Assisted Pipeline for Dynamic Generation of Trustworthy Health Supplement Content at Scale
inferredcites →https://doi.org/10.48550/arxiv.1806.02847openalex-snapshot
A Simple Method for Commonsense Reasoning
inferredcites →https://doi.org/10.48550/arxiv.1810.12885openalex-snapshot
ReCoRD: Bridging the Gap between Human and Machine Commonsense Reading Comprehension
inferredcites →https://doi.org/10.48550/arxiv.2110.07602openalex-snapshot
P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks
inferredcites →https://doi.org/10.48550/arxiv.2111.02643openalex-snapshot
Response Generation with Context-Aware Prompt Learning
inferredcites →https://doi.org/10.48550/arxiv.2204.02311openalex-snapshot
PaLM: Scaling Language Modeling with Pathways
inferredcites →https://doi.org/10.48550/arxiv.2204.04497openalex-snapshot
IDPG: An Instance-Dependent Prompt Generation Method

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Utilizing Reddit Data for Personality Prediction to Enhance Job Fit Assessment in Decision Support Systems
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Efficient Fine-Tuning for Low-Resource Tibetan Pre-trained Language Models
inferred← citeshttps://doi.org/10.1007/978-981-95-3352-7_5openalex-snapshot
Zero-Shot Chain-of-Thought Reasoning Guided by Evolutionary Algorithms in Large Language Models
inferred← citeshttps://doi.org/10.1007/s11704-025-50356-6openalex-snapshot
Human-computer interaction and visualization in natural language generation models: applications, challenges, and opportunities
inferred← citeshttps://doi.org/10.1016/j.bspc.2026.109644openalex-snapshot
Uncertainty-aware variational soft prompt for few-shot tuning in fMRI-based mental state decoding
inferred← citeshttps://doi.org/10.1080/08874417.2025.2512809openalex-snapshot
Personality Classification from Social Media Data Using NLP
inferred← citeshttps://doi.org/10.1080/13875868.2025.2449859openalex-snapshot
Automatically generated place descriptions for accurate location identification: a hybrid approach with rule-based methods and LLM
inferred← citeshttps://doi.org/10.1109/icassp48485.2024.10448111openalex-snapshot
DialCLIP: Empowering Clip As Multi-Modal Dialog Retriever
inferred← citeshttps://doi.org/10.1109/icassp49357.2023.10095738openalex-snapshot
Unified Prompt Learning Makes Pre-Trained Language Models Better Few-Shot Learners
inferred← citeshttps://doi.org/10.1109/taslp.2024.3477330openalex-snapshot
Derivative-Free Optimization for Low-Rank Adaptation in Large Language Models
inferred← citeshttps://doi.org/10.1111/exsy.70049openalex-snapshot
A Particle Swarm Optimization‐Based Approach Coupled With Large Language Models for Prompt Optimization
inferred← citeshttps://doi.org/10.1145/3477495.3531746openalex-snapshot
Relation Extraction as Open-book Examination
inferred← citeshttps://doi.org/10.1145/3716320openalex-snapshot
Reinforced Prompt Personalization for Recommendation with Large Language Models
inferred← citeshttps://doi.org/10.1145/3749372openalex-snapshot
A Unified Prompt-based Framework with Label Semantic Expansion for Joint Multi-intent Detection and Slot Filling.
inferred← citeshttps://doi.org/10.1145/3764579openalex-snapshot
Domain Specialization as the Key to Make Large Language Models Disruptive: A Comprehensive Survey
inferred← citeshttps://doi.org/10.1609/aaai.v38i16.29752openalex-snapshot
Enhancing Low-Resource Relation Representations through Multi-View Decoupling
inferred← citeshttps://doi.org/10.18653/v1/2022.emnlp-main.455openalex-snapshot
Vector-Quantized Input-Contextualized Soft Prompts for Natural Language Understanding
inferred← citeshttps://doi.org/10.18653/v1/2022.findings-emnlp.95openalex-snapshot
Late Prompt Tuning: A Late Prompt Could Be Better Than Many Prompts
inferred← citeshttps://doi.org/10.18653/v1/2022.findings-naacl.3openalex-snapshot
PromptGen: Automatically Generate Prompts using Generative Models
inferred← citeshttps://doi.org/10.18653/v1/2023.acl-long.860openalex-snapshot
Exploring Lottery Prompts for Pre-trained Language Models
inferred← citeshttps://doi.org/10.18653/v1/2023.emnlp-main.22openalex-snapshot
Parameter-efficient Tuning for Large Language Model without Calculating Its Gradients
inferred← citeshttps://doi.org/10.18653/v1/2023.emnlp-main.727openalex-snapshot
SPT: Learning to Selectively Insert Prompts for Better Prompt Tuning
inferred← citeshttps://doi.org/10.18653/v1/2023.findings-acl.150openalex-snapshot
Attribute Controlled Dialogue Prompting
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