Learning Wisdom from Errors: Promoting LLM's Continual Relation Learning through Exploiting Error Cases

TL;DR

This paper introduces an instruction-based contrastive tuning method for Large Language Models to improve continual relation extraction by focusing on error cases, leading to state-of-the-art results.

Contribution

It proposes a novel dual-task fine-tuning and instruction-based contrastive strategy that leverages error cases to enhance LLMs' continual relation learning.

Findings

Achieves new state-of-the-art performance on TACRED and FewRel datasets.

Significantly improves relation extraction accuracy by exploiting error cases.

Demonstrates the effectiveness of error-focused training in continual learning for LLMs.

Abstract

Continual Relation Extraction (CRE) aims to continually learn new emerging relations while avoiding catastrophic forgetting. Existing CRE methods mainly use memory replay and contrastive learning to mitigate catastrophic forgetting. However, these methods do not attach importance to the error cases that can reveal the model's cognitive biases more effectively. To address this issue, we propose an instruction-based continual contrastive tuning approach for Large Language Models (LLMs) in CRE. Different from existing CRE methods that typically handle the training and memory data in a unified manner, this approach splits the training and memory data of each task into two parts respectively based on the correctness of the initial responses and treats them differently through dual-task fine-tuning. In addition, leveraging the advantages of LLM's instruction-following ability, we propose a novel instruction-based contrastive tuning strategy for LLM to continuously correct current cognitive biases with the guidance of previous data in an instruction-tuning manner, which mitigates the gap between old and new relations in a more suitable way for LLMs. We experimentally evaluate our model on TACRED and FewRel, and the results show that our model achieves new state-of-the-art CRE performance with significant improvements, demonstrating the importance of specializing in exploiting error cases.