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Synthetic Worlds for Temporal Evaluation and Knowledge Updating in LLMs
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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cross Abstract: Large language models (LLMs) rely on static pretraining corpora, causing their knowledge to become outdated over time.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 Computation and Language
Oct 28
Parallel Universes for Temporal Evaluation and Knowledge Updating in LLMs
The paper introduces PARALLELEVENTS, a benchmark that simulates realistic future event trajectories to evaluate knowledge updates in large language models without contaminating data or conflicting with existing knowledge. It also presents SYNAPSE, a training framework that uses model-generated synthetic data for instruction tuning, enabling scalable integration of new knowledge. SYNAPSE achieves a 14.23‑point improvement in QA accuracy over previous methods, demonstrating the effectiveness of simulation‑based synthetic training for coherent knowledge insertion.
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
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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
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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.