On a single Thucydides corpus, strong LLMs extract more events without RAG, weak LLMs benefit from simple RAG but collapse under complex RAG, and a Coq translation is proposed to justify RAG-discovered event types.
Event Temporal Relation Extraction based on Retrieval-Augmented on LLMs
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
abstract
Event temporal relation (TempRel) is a primary subject of the event relation extraction task. However, the inherent ambiguity of TempRel increases the difficulty of the task. With the rise of prompt engineering, it is important to design effective prompt templates and verbalizers to extract relevant knowledge. The traditional manually designed templates struggle to extract precise temporal knowledge. This paper introduces a novel retrieval-augmented TempRel extraction approach, leveraging knowledge retrieved from large language models (LLMs) to enhance prompt templates and verbalizers. Our method capitalizes on the diverse capabilities of various LLMs to generate a wide array of ideas for template and verbalizer design. Our proposed method fully exploits the potential of LLMs for generation tasks and contributes more knowledge to our design. Empirical evaluations across three widely recognized datasets demonstrate the efficacy of our method in improving the performance of event temporal relation extraction tasks.
citation-role summary
background 1
citation-polarity summary
fields
cs.CL 1years
2025 1verdicts
REJECT 1roles
background 1polarities
background 1representative citing papers
citing papers explorer
-
Reasoning with RAGged events: RAG-Enhanced Event Knowledge Base Construction and reasoning with proof-assistants
On a single Thucydides corpus, strong LLMs extract more events without RAG, weak LLMs benefit from simple RAG but collapse under complex RAG, and a Coq translation is proposed to justify RAG-discovered event types.