arXiv AI
Jul 24
Moir: Let the Model Direct Its Own Story for Robust Cross-Domain Knowledge Editing
arXiv:2607. 20433v1 Announce Type: cross Abstract: While language models remain frozen at their training state, the world evolves continuously.
Hybrid-Policy Self-Editing for Composable Unstructured Knowledge Editing
arXiv:2608. 11660v1 Announce Type: cross Abstract: Large language models (LLMs) achieve remarkable performance across natural language tasks, yet they are trained on static corpora and their knowledge quickly becomes outdated in a fast-changing world.
Related stories
arXiv AI
Jun 10
Benchmarking Knowledge Editing using Logical Rules
arXiv:2606. 10554v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly deployed in real-world applications that require access to up-to-date knowledge.
arXiv AI
Jun 30
Can Fine-Tuning Erase Your Edits? On the Fragile Coexistence of Knowledge Editing and Adaptation
arXiv:2511. 05852v4 Announce Type: replace-cross Abstract: Knowledge editing (KE) offers a lightweight alternative to retraining for updating large language models (LLMs).
arXiv AI
Jun 2
Revisiting Parameter-Based Knowledge Editing in Large Language Models: Theoretical Limits and Empirical Evidence
arXiv:2606. 00570v1 Announce Type: cross Abstract: Parameter-based knowledge editing updates the internal knowledge of large language models (LLMs) via localized weight modifications and has attracted significant attention.
arXiv AI
Jul 3
Multimodal Knowledge Edit-Scoped Generalization for Online Recursive MLLM Editing
arXiv:2607. 01978v1 Announce Type: new Abstract: Online multimodal knowledge editing requires injecting a continual stream of visual-textual corrections into multimodal large language models (MLLMs) with bounded overhead and minimal disruption to unrelated behaviors.
arXiv AI
Jul 31
ForgetBench: Benchmarking Forgetting Dynamics of Long-Term Parametric Memory in Language Models
arXiv:2607. 26455v1 Announce Type: cross Abstract: 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.
arXiv AI
Jun 24
Representation Interventions Enable Lifelong Knowledge Memory Control in LLMs
arXiv:2511. 20892v4 Announce Type: replace Abstract: Large language models (LLMs) often produce incorrect or outdated content after being employed.
Hugging Face Trending Papers
Jul 29
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.
arXiv AI
Jun 16
Edit Knowledge, Not Just Facts via Multi-Step Reasoning over Background Stories
arXiv:2602. 02028v2 Announce Type: replace Abstract: Enabling artificial intelligence systems, particularly large language models, to update knowledge and flexibly apply it during reasoning remains a central challenge.
arXiv Machine Learning
Jun 26
Reproducibility Study of "AlphaEdit: Null-Space Constrained Knowledge Editing for Language Models"
arXiv:2606. 26783v1 Announce Type: new Abstract: Fang et al.
arXiv Machine Learning
Jun 25
Exposing the Illusion of Erasure in Knowledge Editing for LLMs
arXiv:2606. 23276v2 Announce Type: replace Abstract: Knowledge Editing (KE) has emerged as a frontier for updating specific facts in LLMs without costly retraining, but its reliability and underlying mechanisms remain poorly understood.
arXiv Machine Learning
Jul 22
More Edits, More Stable: Understanding the Lifelong Normalization in Sequential Model Editing
arXiv:2605. 11836v2 Announce Type: replace Abstract: Lifelong Model Editing aims to continuously update evolving facts in Large Language Models while preserving unrelated knowledge and general capabilities, yet it remains plagued by catastrophic forgetting and model collapse.