The study investigates how well language models can generalize from fine‑tuned facts by distinguishing active facts—those that support further learning—from inert facts. Using a novel relabeling experiment (e.g., mapping Paris → Europe to Paris → crab), the authors fine‑tune models on a subset of subjects and test whether the learned mapping extends to unseen subjects sharing the same original answer. Across eleven models and 70 relations, they find a strong correlation (Pearson r = 0.87–0.92) between the linear readability of the original relation in pretrained representations and the model’s ability to generalize, a relationship that persists after controlling for various factors and remains robust to label randomization and semantic category changes.
By Faridoun Mehri, Hinrich Sch\"utze
arXiv:2409. 02228v2 Announce Type: replace Abstract: When language models (LMs) are trained to forget (or "unlearn'') a skill, how precisely does their behavior change?
By Eric Zhang, Leshem Choshen, Jacob Andreas
Activation verbalizers translate the internal representations of large language models into human-readable text. The recently proposed natural language autoencoders (NLAs) learn this by reconstructing...
arXiv:2606. 11198v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG) systems inject external knowledge to improve LLM outputs, yet the format of injected content -- distinct from its semantic relevance -- can independently distort the model's attention distribution.
By Yuqi Zhang, Di Zhang
arXiv:2609.23065v1 Announce Type: cross
Abstract: Chain-of-thought (CoT) can sound plausible yet be unfaithful to the model's underlying reasoning. Most prior work probes CoT faithfulness through inp...
By Qianli Wang, Yilong Wang, Dennis Wei, Jingyi Sun, Simon Ostermann, Pepa Atanasova, Nils Feldhus
arXiv:2606.00284v2 Announce Type: replace
Abstract: While continual pretraining (CPT) is a practical way to extend large language models to new languages, na\"ive finetuning often erodes existing cap...
By Sanchit Ahuja, Terra Blevins