mlsanityData to 7 Oct 2026

Dynamic Few-Shot Learning for Knowledge Graph Question Answering

arXiv 2407.01409 ↗First posted 1 Jul 2024cs.CL cs.AIcited by 4

Not on the map. Why?

Abstract (arXiv)

Large language models present opportunities for innovative Question Answering over Knowledge Graphs (KGQA). However, they are not inherently designed for query generation. To bridge this gap, solutions have been proposed that rely on fine-tuning or ad-hoc architectures, achieving good results but limited out-of-domain distribution generalization. In this study, we introduce a novel approach called Dynamic Few-Shot Learning (DFSL). DFSL integrates the efficiency of in-context learning and semantic similarity and provides a generally applicable solution for KGQA with state-of-the-art performance. We run an extensive evaluation across multiple benchmark datasets and architecture configurations.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 23 of its 43 references that are in the map. First posted on arXiv 1 Jul 2024.

Builds on

23 of 43 references.

References · 23

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
  3. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 13,958
  4. 2021Evaluating Large Language Models Trained on Codecited by 8,053
  5. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,874
  6. 2023Code Llama: Open Foundation Models for Codecited by 2,105
  7. 2017Get To The Point: Summarization with Pointer-Generator Networkscited by 1,281
  8. 2021What Makes Good In-Context Examples for GPT-3?cited by 1,184
  9. 2022A Survey on In-context Learningcited by 717
  10. 2022Demystifying Prompts in Language Models via Perplexity Estimationcited by 215
  11. 2022A Review of Sparse Expert Models in Deep Learningcited by 147
  12. 2022Diverse Demonstrations Improve In-context Compositional Generalizationcited by 136
11 more
  1. 2023Knowledge-Augmented Language Model Prompting for Zero-Shot Knowledge Graph Question Answeringcited by 119
  2. 2023Few-shot In-context Learning for Knowledge Base Question Answeringcited by 74
  3. 2022Self-Generated In-Context Learning: Leveraging Auto-regressive Language Models as a Demonstration Generatorcited by 68
  4. 2023ACT-SQL: In-Context Learning for Text-to-SQL with Automatically-Generated Chain-of-Thoughtcited by 65
  5. 2023Retrieve-Rewrite-Answer: A KG-to-Text Enhanced LLMs Framework for Knowledge Graph Question Answeringcited by 64
  6. 2023Enhancing Few-shot Text-to-SQL Capabilities of Large Language Models: A Study on Prompt Design Strategiescited by 44
  7. 2022Modern Baselines for SPARQL Semantic Parsingcited by 34
  8. 2021FewshotQA: A simple framework for few-shot learning of question answering tasks using pre-trained text-to-text modelscited by 26
  9. 2024Enhancing Complex Question Answering over Knowledge Graphs through Evidence Pattern Retrievalcited by 16
  10. 2021A Chinese Multi-type Complex Questions Answering Dataset over Wikidatacited by 5
  11. 2023Gotta: Generative Few-shot Question Answering by Prompt-based Cloze Data Augmentationcited by 5

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 1; 2026: 1.

4 papers.

  1. 2025Efficient and Transferable Agentic Knowledge Graph RAG via Reinforcement Learningcited by 5
  2. 2024Balancing Efficiency and Effectiveness: An LLM-Infused Approach for Optimized CTR Predictioncited by 3
  3. 2024Language Models Learn Metadata: Political Stance Detection Case Studycited by 0
  4. 2026Scalable Identification and Prioritization of Requisition-Specific Personal Competencies Using Large Language Modelscited by 0