KLoB: a Benchmark for Assessing Knowledge Locating Methods in Language Models
This work addresses the need for standardized evaluation in the Locate-Then-Edit paradigm for researchers in natural language processing and AI, though it is incremental as it builds on existing methods without introducing new ones.
The authors tackled the problem of evaluating knowledge locating methods in language models by introducing KLoB, a benchmark that assesses three essential properties for reliability and provides a method to test the locality hypothesis of factual knowledge.
Recently, Locate-Then-Edit paradigm has emerged as one of the main approaches in changing factual knowledge stored in the Language models. However, there is a lack of research on whether present locating methods can pinpoint the exact parameters embedding the desired knowledge. Moreover, although many researchers have questioned the validity of locality hypothesis of factual knowledge, no method is provided to test the a hypothesis for more in-depth discussion and research. Therefore, we introduce KLoB, a benchmark examining three essential properties that a reliable knowledge locating method should satisfy. KLoB can serve as a benchmark for evaluating existing locating methods in language models, and can contributes a method to reassessing the validity of locality hypothesis of factual knowledge. KLoB is publicly available at an anonymous GitHub: \url{https://github.com/anon6662/KLoB}.