Abstract Language models have achieved remarkable performance on a wide range of tasks
that require natural language understanding. Nevertheless, state-of-the-art models have …

… See Appendix C.1 for details on the datasets and the refusal classifier we used to determine
when a … We describe how we constructed the many-shot prompts in Appendix C.2. …

We present a system for training deep neural networks for object detection using synthetic
images. To handle the variability in real-world data, the system relies upon the technique of …

… Further analysis of preference model scores for our models can be found in Appendix C.
For both chain-ofthought and distilled chain-of-thought backdoored models, preference model …

The ability to extrapolate from short problem instances to longer ones is an important form of
out-of-distribution generalization in reasoning tasks, and is crucial when learning from …

… where n and |C| denote the size of the player set N and the size of the coalition C, respectively.
• The core of the cooperative game presents a situation where there is no coalition that …

… where the function count simply counts the number of documents the token t appears in and
C denotes the … For full details on the experimental setup, we refer readers to Appendix C. …

… 1 from Condat (2016) (see Appendix C) to project the weight matrices onto the L∞ ball. …
and Bowie, C. An iterative algorithm for computing the best estimate of an orthogonal matrix. …

… A blood sample was obtained to measure serum concentrations of fasting glucose and
insulin, HDL cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and TGs together …

BACKGROUND To the authors' knowledge, outcomes and prognostic tools have yet to be
clearly defined in patients with metastatic renal cell carcinoma (mRCC) who are treated with …