7 papers
CMCNet: Aligning Ultrasound Image Embeddings with Textual TI-RADS Representations for Fine-Grained Thyroid Classification
Bingxin Yu, Xueli Wang, Jerry Zhou +6
Ultrasound is the primary imaging modality for assessing thyroid nodules, and the ACR TI-RADS framework standardizes diagnosis through five ultrasound feature categories that are a…
Internal Data Repetition Destroys Language Models
Jessica Chudnovsky, Joshua Kazdan, Noam Levi +6
Language models are running out of high-quality training data, and even aggressively deduplicated corpora retain some amount of repetition. Earlier controlled studies predated Chin…
More Bang for the Buck: Improving the Inference of Large Language Models at a Fixed Budget using Reset and Discard (ReD)
Sagi Meir, Tommer D. Keidar, Noam Levi +2
The performance of large language models (LLMs) on verifiable tasks is usually measured by pass@k, the probability of answering a question correctly at least once in k trials. At a…
Pretraining Scaling Laws for Generative Evaluations of Language Models
Rylan Schaeffer, Noam Levi, Brando Miranda +1
Neural scaling laws have driven the field's ever-expanding exponential growth in parameters, data and compute. While scaling behaviors for pretraining losses and discriminative ben…
Learning Shrinks the Hard Tail: Training-Dependent Inference Scaling in a Solvable Linear Model
Noam Levi
We analyze neural scaling laws in a solvable model of last-layer fine-tuning where targets have intrinsic, instance-heterogeneous difficulty. In our Latent Instance Difficulty (LID…
Efficient Prediction of Pass@k Scaling in Large Language Models
Joshua Kazdan, Rylan Schaeffer, Youssef Allouah +4
Assessing the capabilities and risks of frontier AI systems is a critical area of research, and recent work has shown that repeated sampling from models can dramatically increase b…