Hugging Face Trending Papers
Synthetic Worlds for Temporal Evaluation and Knowledge Updating in LLMs
Read the original on Hugging Face Trending Papers →The Flow has not summarised this story yet — read it at Hugging Face Trending Papers.
Related stories
arXiv Machine Learning
Sep 2
Synthetic Worlds for Temporal Evaluation and Knowledge Updating in LLMs
arXiv:2609.00184v1 Announce Type: cross Abstract: Large language models (LLMs) rely on static pretraining corpora, causing their knowledge to become outdated over time. Existing approaches for evalua...
arXiv AI
Sep 2
KItCAT: Knowledge Injection via Input Corruption for Auto-regressive Training
arXiv:2609.00082v1 Announce Type: cross Abstract: LLMs acquire vast amounts of knowledge during pre-training, but often lack the specialized knowledge needed to answer questions from niche sources su...
arXiv AI
Aug 11
SPA: A Simple but Tough-to-Beat Baseline for Knowledge Injection
arXiv:2603. 22213v2 Announce Type: replace-cross Abstract: While large language models (LLMs) are pretrained on massive amounts of data, their knowledge coverage remains incomplete in specialized, data-scarce domains, motivating extensive efforts to study synthetic data generation for knowledge injection.
arXiv AI
Sep 15
Towards Evolving Context Parameterization for Large Language Models
arXiv:2609.14168v1 Announce Type: cross Abstract: Context parameterization enables large language models (LLMs) to internalize contexts into reusable model parameters, avoiding repeated processing ac...
arXiv AI
Jul 15
Scaling Point-in-Time Language Models
arXiv:2607. 11889v1 Announce Type: cross Abstract: Large language models trained on unrestricted internet corpora inevitably embed information from the future, introducing lookahead bias that compromises the validity of backtests and causal inference in finance and the social sciences.
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arXiv:2511. 20892v4 Announce Type: replace Abstract: Large language models (LLMs) often produce incorrect or outdated content after being employed.