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Memory Editing (ME) has emerged as an efficient method to modify erroneous facts or inject new facts into Large Language Models (LLMs). Two mainstream ME methods exist: parameter-modifying ME and parameter-preserving ME (integrating extra modules while preserving original parameters). Regrettably, previous studies on ME evaluation have two critical limitations: (i) evaluating LLMs with single edit only, neglecting the need for continuous editing, and (ii) evaluations focusing solely on basic factual triples, overlooking broader LLM capabilities like logical reasoning and reading understanding. This study addresses these limitations with contributions threefold: (i) We explore how ME affects a wide range of fundamental capabilities of LLMs under sequential editing. Experimental results reveal an intriguing phenomenon: Most parameter-modifying ME consistently degrade performance across all tasks after a few sequential edits. In contrast, parameter-preserving ME effectively maintains LLMs’ fundamental capabilities but struggles to accurately recall edited knowledge presented in a different format. (ii) We extend our evaluation to different editing settings, such as layers to edit, model size, instruction tuning, etc. Experimental findings indicate several strategies that can potentially mitigate the adverse effects of ME. (iii) We further explain why parameter-modifying damages LLMs from three dimensions: parameter changes after editing, language modeling capability, and the in-context learning capability. Our in-depth study advocates more careful use of ME in real-world scenarios. 

Abstract Hypoxia inducible factor 1 alpha (HIF1A) is a transcription factor (TF) that forms highly structural and functional protein–protein interactions with other TFs to promote gene expression in hypoxic cancer cells. However, despite the importance of these TF-TF interactions, we still lack a comprehensive view of many of the TF cofactors involved and how they cooperate. In this study, we systematically studied HIF1A cofactors in eight cancer cell lines using the computational motif mining tool, SIOMICS, and discovered 201 potential HIF1A cofactors, which included 21 of the 29 known HIF1A cofactors in public databases. These 201 cofactors were statistically and biologically significant, with 19 of the top 37 cofactors in our study directly validated in the literature. The remaining 18 were novel cofactors. These discovered cofactors can be essential to HIF1A’s regulatory functions and may lead to the discovery of new therapeutic targets in cancer treatment. 

Recent geochemical evidence confirms the oxidized nature of arc magmas, but the underlying processes that regulate the redox state of the subarc mantle remain yet to be determined. We established a link between deep subduction-related fluids derived from dehydration of serpentinite ± altered oceanic crust (AOC) using B isotopes and B/Nb as fluid proxies, and the oxidized nature of arc magmas as indicated by Cu enrichment during magma evolution and V/Yb. Our results suggest that arc magmas derived from source regions influenced by a greater serpentinite (±AOC) fluid component record higher oxygen fugacity. The incorporation of this component into the subarc mantle is controlled by the subduction system’s thermodynamic conditions and geometry. Our results suggest that the redox state of the subarc mantle is not homogeneous globally: Primitive arc magmas associated with flat, warm subduction are less oxidized overall than those generated in steep, cold subduction zones.