This study conducts a detailed analysis of the side effects of rank-one knowledge editing using language models with controlled knowledge. The analysis focuses on each element of knowledge triples (subject, relation, object) and examines two aspects: “knowledge that causes large side effects when edited” and “knowledge that is affected by the side effects.” Our findings suggest that editing knowledge with subjects that have relationships with numerous objects or are robustly embedded within the LM may trigger extensive side effects. Furthermore, we demonstrate that the similarity between relation vectors, the density of object vectors, and the distortion of knowledge representations are closely related to how susceptible knowledge is to editing influences. The findings of this research provide new insights into the mechanisms of side effects in LM knowledge editing and indicate specific directions for developing more effective and reliable knowledge editing methods.

Temporal reasoning and knowledge are essential capabilities for language models (LMs).While much prior work has analyzed and improved temporal reasoning in LMs, most studies have focused solely on the Gregorian calendar.However, many non-Gregorian systems, such as the Japanese, Hijri, and Hebrew calendars, are in active use and reflect culturally grounded conceptions of time.If and how well current LMs can accurately handle such non-Gregorian calendars has not been evaluated so far.Here, we present a systematic evaluation of how well language models handle one such non-Gregorian system: the Japanese *wareki*.We create datasets that require temporal knowledge and reasoning in using *wareki* dates. Evaluating open and closed LMs, we find that some models can perform calendar conversions, but GPT-4o, Deepseek V3, and even Japanese-centric models struggle with Japanese calendar arithmetic and knowledge involving *wareki* dates.Error analysis suggests corpus frequency of Japanese calendar expressions and a Gregorian bias in the model’s knowledge as possible explanations.Our results show the importance of developing LMs that are better equipped for culture-specific tasks such as calendar understanding.

Many linguistic expressions have idiomatic and literal interpretations, and the automatic distinction of these two interpretations has been studied for decades. Recent research has shown that contextualized word embeddings derived from masked language models (MLMs) can give promising results for idiom token classification. This indicates that contextualized word embedding alone contains information about whether the word is being used in a literal sense or not. However, we believe that more types of information can be derived from MLMs and that leveraging such information can improve idiom token classification. In this paper, we leverage three types of embeddings from MLMs; uncontextualized token embeddings and masked token embeddings in addition to the standard contextualized word embeddings and show that the newly added embeddings significantly improve idiom token classification for both English and Japanese datasets.