# Few-Shot, No Problem: Descriptive Continual Relation Extraction

arXiv: https://arxiv.org/abs/2502.20596 · HTML: https://mlsanity.com/paper/2502.20596/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 24 references that are in the map.
- First posted on arXiv 27 Feb 2025.

## Builds on (most cited first, 12 of 12)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context](https://mlsanity.com/paper/2403.05530/) (2024)
- [Prototypical Networks for Few-shot Learning](https://mlsanity.com/paper/1703.05175/) (2017)
- [Gradient Episodic Memory for Continual Learning](https://mlsanity.com/paper/1706.08840/) (2017)
- [Continual Learning with Deep Generative Replay](https://mlsanity.com/paper/1705.08690/) (2017)
- [FewRel: A Large-Scale Supervised Few-Shot Relation Classification Dataset with State-of-the-Art Evaluation](https://mlsanity.com/paper/1810.10147/) (2018)
- [Preserving Generalization of Language models in Few-shot Continual Relation Extraction](https://mlsanity.com/paper/2410.00334/) (2024)
- [KOPPA: Improving Prompt-based Continual Learning with Key-Query Orthogonal Projection and Prototype-based One-Versus-All](https://mlsanity.com/paper/2311.15414/) (2023)
- [Adaptive Prompting for Continual Relation Extraction: A Within-Task Variance Perspective](https://mlsanity.com/paper/2412.08285/) (2024)
- [Synergistic Anchored Contrastive Pre-training for Few-Shot Relation Extraction](https://mlsanity.com/paper/2312.12021/) (2023)
- [Making Pre-trained Language Models Better Continual Few-Shot Relation Extractors](https://mlsanity.com/paper/2402.15713/) (2024)

## Built on by (most cited first, 2 of 2)

- [WAVE++: Capturing Within-Task Variance for Continual Relation Extraction with Adaptive Prompting](https://mlsanity.com/paper/2505.13944/) (2025)
- [Spectral-Aware Analytic Class-Incremental Learning for Long-Tailed Distributions](https://mlsanity.com/paper/2607.22931/) (2026)

## Abstract (arXiv)

Few-shot Continual Relation Extraction is a crucial challenge for enabling AI systems to identify and adapt to evolving relationships in dynamic real-world domains. Traditional memory-based approaches often overfit to limited samples, failing to reinforce old knowledge, with the scarcity of data in few-shot scenarios further exacerbating these issues by hindering effective data augmentation in the latent space. In this paper, we propose a novel retrieval-based solution, starting with a large language model to generate descriptions for each relation. From these descriptions, we introduce a bi-encoder retrieval training paradigm to enrich both sample and class representation learning. Leveraging these enhanced representations, we design a retrieval-based prediction method where each sample "retrieves" the best fitting relation via a reciprocal rank fusion score that integrates both relation description vectors and class prototypes. Extensive experiments on multiple datasets demonstrate that our method significantly advances the state-of-the-art by maintaining robust performance across sequential tasks, effectively addressing catastrophic forgetting.