11 papers
Spokes: Optimizing for Diverse Pretraining Data Selection
Clarence Lee, Yejin Choi, Luke Zettlemoyer +2
Diversity plays a critical role in data selection, improving performance under fixed data budgets by reducing redundancy and repetition. However, optimizing for diversity is inhere…
DR Tulu: Reinforcement Learning with Evolving Rubrics for Deep Research
Rulin Shao, Akari Asai, Shannon Zejiang Shen +18
Deep research agents perform multi-step research to produce long-form, well-attributed answers. However, most open deep research agents are trained on easily verifiable short-form…
A False Sense of Privacy: Evaluating Textual Data Sanitization Beyond Surface-level Privacy Leakage
Rui Xin, Niloofar Mireshghallah, Shuyue Stella Li +6
Sanitizing sensitive text data typically involves removing personally identifiable information (PII) or generating synthetic data under the assumption that these methods adequately…
2 OLMo 2 Furious
Team OLMo, Pete Walsh, Luca Soldaini +40
We present OLMo 2, the next generation of our fully open language models. OLMo 2 includes a family of dense autoregressive language models at 7B, 13B and 32B scales with fully rele…
Precise Information Control in Long-Form Text Generation
Jacqueline He, Howard Yen, Margaret Li +7
A central challenge in language models (LMs) is faithfulness hallucination: the generation of information unsubstantiated by input context. To study this problem, we propose Precis…
ParaPO: Aligning Language Models to Reduce Verbatim Reproduction of Pre-training Data
Tong Chen, Faeze Brahman, Jiacheng Liu +5
Language models (LMs) can memorize and reproduce segments from their pretraining data verbatim even in non-adversarial settings, raising concerns about copyright, plagiarism, priva…