arXiv AI
Predicting how model propensities generalize out of distribution
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
Related stories
arXiv Machine Learning
Oct 16
INCONVENIENT: A Benchmark to Assess LLM Generalization Capabilities from Convenience Samples
INCONVENIENT is a new benchmark that evaluates how machine‑learning models, especially large language models, generalize when trained on convenience samples that bias feature–target relationships. Unlike prior distribution‑shift tests that rely on natural domain splits, INCONVENIENT explicitly perturbs these relationships while keeping the deployment population unchanged, allowing controlled study of shift severity and direction. The benchmark covers 200 tabular classification and regression datasets and produces about 15,500 shifted training distributions, revealing significant performance degradation.
arXiv AI
Sep 30
The Unequal Influence of Bad Advice: Using Training Data Attribution to Modulate Emergent Misalignment
arXiv:2609.37914v1 Announce Type: cross Abstract: Fine-tuning large language models on narrow, misaligned tasks can undo their post-training alignment and induce novel misaligned behaviors -- a pheno...
arXiv AI
Sep 1
Emergent Misalignment Is Not Magical
arXiv:2608.29118v1 Announce Type: new Abstract: Fine-tuning large language models (LLMs) on narrowly harmful datasets can lead to misalignment broadly, a phenomenon known as emergent misalignment (EM...
arXiv AI
Sep 15
LLM Probability Concentration: How Alignment Shrinks the Generative Horizon
arXiv:2506.17871v4 Announce Type: replace-cross Abstract: Despite their impressive capabilities, aligned large language models (LLMs) often generate outputs that lack diversity. What drives this cons...
arXiv Computation and Language
Aug 24
When the Feature Pool Goes Algorithmic: Extending Mufwene's Ecology of Language Evolution to LLM-Mediated Exposure
arXiv:2608.21088v1 Announce Type: new Abstract: Mufwene's ecological model locates language evolution in competition among variants contributed by individual idiolects and in speakers' selection from...
arXiv Machine Learning
Aug 12
Mapping and Measuring the Behavioral Evolution of Large Language Models
arXiv:2608. 11027v1 Announce Type: new Abstract: Benchmark leaderboards summarize how well a language model performs, but not how its behavior relates to that of other models or changes across generations.