Analyzing Temporal Complex Events with Large Language Models? A Benchmark towards Temporal, Long Context Understanding
This work addresses the need for efficient analysis of temporal events in online news, but it is incremental as it focuses on benchmarking existing methods rather than introducing a new paradigm.
The paper tackles the problem of analyzing complex events from many news articles over time by proposing a benchmark, TCELongBench, to evaluate Large Language Models (LLMs) on tasks like reading comprehension and temporal sequencing, finding that models with retrieval-augmented generation perform comparably to those with long context windows.
The digital landscape is rapidly evolving with an ever-increasing volume of online news, emphasizing the need for swift and precise analysis of complex events. We refer to the complex events composed of many news articles over an extended period as Temporal Complex Event (TCE). This paper proposes a novel approach using Large Language Models (LLMs) to systematically extract and analyze the event chain within TCE, characterized by their key points and timestamps. We establish a benchmark, named TCELongBench, to evaluate the proficiency of LLMs in handling temporal dynamics and understanding extensive text. This benchmark encompasses three distinct tasks - reading comprehension, temporal sequencing, and future event forecasting. In the experiment, we leverage retrieval-augmented generation (RAG) method and LLMs with long context window to deal with lengthy news articles of TCE. Our findings indicate that models with suitable retrievers exhibit comparable performance with those utilizing long context window.