EvEval: A Comprehensive Evaluation of Event Semantics for Large Language Models
This addresses the problem of evaluating event semantics in NLP for researchers, but it is incremental as it builds on existing datasets and frameworks.
The authors tackled the lack of a comprehensive evaluation framework for event semantic processing in large language models by introducing EVEVAL, a benchmark covering 8 datasets, and found several noteworthy results from extensive experiments.
Events serve as fundamental units of occurrence within various contexts. The processing of event semantics in textual information forms the basis of numerous natural language processing (NLP) applications. Recent studies have begun leveraging large language models (LLMs) to address event semantic processing. However, the extent that LLMs can effectively tackle these challenges remains uncertain. Furthermore, the lack of a comprehensive evaluation framework for event semantic processing poses a significant challenge in evaluating these capabilities. In this paper, we propose an overarching framework for event semantic processing, encompassing understanding, reasoning, and prediction, along with their fine-grained aspects. To comprehensively evaluate the event semantic processing abilities of models, we introduce a novel benchmark called EVEVAL. We collect 8 datasets that cover all aspects of event semantic processing. Extensive experiments are conducted on EVEVAL, leading to several noteworthy findings based on the obtained results.