G2S: A General-to-Specific Learning Framework for Temporal Knowledge Graph Forecasting with Large Language Models
This work addresses a bottleneck in TKG forecasting for AI researchers by enhancing model generalization, though it is incremental as it builds on existing LLM-based methods.
The paper tackles the problem of forecasting over Temporal Knowledge Graphs (TKGs) by proposing a General-to-Specific learning framework (G2S) that disentangles general patterns and scenario-specific knowledge, which improves the generalization abilities of Large Language Models (LLMs) on this task.
Forecasting over Temporal Knowledge Graphs (TKGs) which predicts future facts based on historical ones has received much attention. Recent studies have introduced Large Language Models (LLMs) for this task to enhance the models' generalization abilities. However, these models perform forecasting via simultaneously learning two kinds of entangled knowledge in the TKG: (1) general patterns, i.e., invariant temporal structures shared across different scenarios; and (2) scenario information, i.e., factual knowledge engaged in specific scenario, such as entities and relations. As a result, the learning processes of these two kinds of knowledge may interfere with each other, which potentially impact the generalization abilities of the models. To enhance the generalization ability of LLMs on this task, in this paper, we propose a General-to-Specific learning framework (G2S) that disentangles the learning processes of the above two kinds of knowledge. In the general learning stage, we mask the scenario information in different TKGs and convert it into anonymous temporal structures. After training on these structures, the model is able to capture the general patterns across different TKGs. In the specific learning stage, we inject the scenario information into the structures via either in-context learning or fine-tuning modes. Experimental results show that G2S effectively improves the generalization abilities of LLMs.