GDLLM improves event temporal relation extraction by feeding LLM-generated probability distributions into a graph attention network, achieving state-of-the-art micro-F1 scores on TB-Dense and MATRES.
MAQInstruct: Instruction-based Unified Event Relation Extraction
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abstract
Extracting event relations that deviate from known schemas has proven challenging for previous methods based on multi-class classification, MASK prediction, or prototype matching. Recent advancements in large language models have shown impressive performance through instruction tuning. Nevertheless, in the task of event relation extraction, instruction-based methods face several challenges: there are a vast number of inference samples, and the relations between events are non-sequential. To tackle these challenges, we present an improved instruction-based event relation extraction framework named MAQInstruct. Firstly, we transform the task from extracting event relations using given event-event instructions to selecting events using given event-relation instructions, which reduces the number of samples required for inference. Then, by incorporating a bipartite matching loss, we reduce the dependency of the instruction-based method on the generation sequence. Our experimental results demonstrate that MAQInstruct significantly improves the performance of event relation extraction across multiple LLMs.
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cs.CL 1years
2025 1verdicts
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GDLLM: A Global Distance-aware Modeling Approach Based on Large Language Models for Event Temporal Relation Extraction
GDLLM improves event temporal relation extraction by feeding LLM-generated probability distributions into a graph attention network, achieving state-of-the-art micro-F1 scores on TB-Dense and MATRES.