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.
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.
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arXiv AI
Sep 25
ALOE: Semantically Addressed Low-Rank Operators for Knowledge Editing
The paper introduces ALOE, a method for knowledge editing that learns semantic addresses from paraphrases and hard negatives, aligns them with autoregressive hidden states, and embeds a gated low‑rank operator within a single MLP layer. This design allows the edited model to run in one forward pass without external retrievers or routers. Experiments on CounterFact, ZSRE, and KnowEdit show high efficacy (0.955–0.999) and locality (0.981–1.000) across 7–8B model families, with analyses indicating effective separation of edits and suppression of out‑of‑scope activation.
arXiv AI
4d ago
Generalizable Lifelong Model Editing via Preference Optimization
The paper introduces GLIME, a lifelong model editing framework that integrates knowledge editing with preference optimization to handle continual updates in large language models. GLIME employs replay-based editing and a gradient constraint to prevent overfitting to target prompts and preserve previously edited knowledge. Experiments demonstrate that GLIME enhances knowledge generalization while maintaining editing performance and overall model capabilities.
arXiv AI
Aug 13
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performance across natural language tasks, yet they are trained on static corpora and their knowledge quickly becomes outdated in a fast-changing world.
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
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.
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 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 AI
Jun 19
LOKI: Memory-Free Null-Space Constrained Lifelong Knowledge Editing
arXiv:2606. 19679v1 Announce Type: cross Abstract: Lifelong knowledge editing aims to efficiently and sequentially update language models over time, as new knowledge becomes available or when the model makes mistakes, while preserving acceptable performance on past knowledge.
arXiv Computation and Language
Sep 22
Diagnose, Then Repair: A Two-Stage MQM-Guided Post-Editing Framework for Domain-Specific Machine Translation
arXiv:2609.22793v1 Announce Type: new Abstract: LLM-based machine translation evaluation can closely match human judgments, but in practice it remains largely diagnostic, with the signals rarely tran...
arXiv Computation and Language
Sep 2
InComeS: Integrating Compression and Selection Mechanisms into LLMs for Efficient Model Editing
InComeS is a framework that improves large language models (LLMs) for model editing by compressing editing contexts into a key‑value cache associated with a special gist token. It adds cross‑attention modules to dynamically select the most relevant information from these gist pools, allowing efficient handling of multiple edits beyond the LLM’s context window. Experiments on diverse editing benchmarks show that InComeS enhances both effectiveness and efficiency compared to existing methods.
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.