ForgetBench: Benchmarking Forgetting Dynamics of Long-Term Parametric Memory in Language Models
Large language models (LLMs) have demonstrated strong capabilities in knowledge acquisition and reasoning, yet their ability to retain previously acquired knowledge under repeated updates remains insufficiently understood. Existing evaluation paradigms primarily focus on single-step reasoning or static knowledge editing, which fail to capture the temporal dynamics of knowledge retention and degradation during continual model modification. In this work, we propose \textbf{ForgetBench}, a benchmark for measuring forgetting dynamics in the long-term parametric memory of LLMs under continual knowledge editing. ForgetBench introduces two complementary evaluation paradigms, namely concept-based...
| Authors | Ruxi Gu, Zhenliang Zhang, Zilei Wang et al. |
|---|---|
| Primary category | cs.CL |
| Published | 29 July 2026 |
| Last updated at source | 10 October 2026 |
Silytics shows public metadata from arXiv and links to the original. Information may be incomplete or out of date. Last refreshed 17 October 2026. Licenses and terms of the original apply to the work itself. Not financial, legal or professional advice.