Few-Shot, No Problem: Descriptive Continual Relation Extraction
Authors: Nguyen Xuan Thanh, Anh Duc Le, Quyen Tran, Thanh-Thien Le, Linh Ngo Van, Thien Huu Nguyen
AAAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 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. |
| Researcher Affiliation | Collaboration | 1Oraichain Labs, 2Hanoi University of Science and Technology, 3Vin AI Research, 4University of Oregon |
| Pseudocode | Yes | Algorithm 1 outlines the end-to-end training process at each task T j, with Φj 1 denoting the model after training on the previous j 1 tasks. In line with memory-based continual learning methods, we maintain a memory buffer Mj 1 that stores a few representative samples from all previous tasks T 1, . . . , T j 1, along with a relation description set Ej 1 that holds the descriptions of all previously encountered relations. |
| Open Source Code | No | The paper does not contain any explicit statement about providing access to source code or a link to a code repository. |
| Open Datasets | Yes | We conduct experiments using two pre-trained language models, BERT (Devlin et al. 2019) and LLM2Vec (Behnam Ghader et al. 2024), on two widely used benchmark datasets for Relation Extraction: Few Rel (Han et al. 2018) and TACRED (Zhang et al. 2017). |
| Dataset Splits | Yes | In Few-Shot Continual Relation Extraction (FCRE), a model must continuously assimilate new knowledge from a sequential series of tasks. For each t-th task, the model undergoes training on the dataset Dt = {(xt i, yt i)}N K i=1 . Here, N represents the number of relations in the task Rt, and K denotes the limited number of samples per relation, reflecting the few-shot learning scenario. This type of task setup is referred to as N-way-K-shot (Chen, Wu, and Shi 2023a). |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory amounts) used for running the experiments. |
| Software Dependencies | No | The paper mentions using BERT and LLM2Vec as pre-trained language models but does not provide specific version numbers for any software dependencies or libraries used in their implementation. |
| Experiment Setup | Yes | The soft prompt phrase length is set to 3 tokens, meaning n0, n1, n2 and n3 correspond to the values of 3, 6, 9, and 12, respectively. |