SUMIE: A Synthetic Benchmark for Incremental Entity Summarization
This addresses the problem of maintaining accurate knowledge in AI systems for researchers and developers, though it is incremental as it focuses on dataset creation rather than a new method.
The paper tackles the lack of a dataset for testing incremental entity summarization in language models by introducing SUMIE, a synthetic benchmark that exposes real-world challenges, with state-of-the-art LLMs achieving an F1 score of only 80.4% on the task.
No existing dataset adequately tests how well language models can incrementally update entity summaries - a crucial ability as these models rapidly advance. The Incremental Entity Summarization (IES) task is vital for maintaining accurate, up-to-date knowledge. To address this, we introduce SUMIE, a fully synthetic dataset designed to expose real-world IES challenges. This dataset effectively highlights problems like incorrect entity association and incomplete information presentation. Unlike common synthetic datasets, ours captures the complexity and nuances found in real-world data. We generate informative and diverse attributes, summaries, and unstructured paragraphs in sequence, ensuring high quality. The alignment between generated summaries and paragraphs exceeds 96%, confirming the dataset's quality. Extensive experiments demonstrate the dataset's difficulty - state-of-the-art LLMs struggle to update summaries with an F1 higher than 80.4%. We will open source the benchmark and the evaluation metrics to help the community make progress on IES tasks.