models openreview - Robuta Search

https://openreview.net/forum?id=yzkSU5zdwD Emergent Abilities of Large Language Models | OpenReview Scaling up language models has been shown to predictably improve performance and sample efficiency on a wide range of downstream tasks. This paper instead... large language models emergent abilities openreview https://openreview.net/forum?id=WCRQFlji2q Do I Know This Entity? Knowledge Awareness and Hallucinations in Language Models | OpenReview Hallucinations in large language models are a widespread problem, yet the mechanisms behind whether models will hallucinate are poorly understood, limiting our... language models openreview knowledge awareness entity hallucinations https://openreview.net/forum?id=m6xyTie61H Eliciting Language Model Behaviors using Reverse Language Models | OpenReview Despite advances in fine-tuning methods, language models (LMs) continue to output toxic and harmful responses on worst-case inputs, including adversarial... language model using reverse models openreview eliciting behaviors https://openreview.net/forum?id=SylkYeHtwr SUMO: Unbiased Estimation of Log Marginal Probability for Latent Variable Models | OpenReview We create an unbiased estimator for the log probability of latent variable models, extending such models to a larger scope of applications. latent variable models sumo unbiased estimation log https://openreview.net/forum?id=yaqPf0KAlN Omni-MATH: A Universal Olympiad Level Mathematic Benchmark for Large Language Models | OpenReview Recent advancements in large language models (LLMs) have led to significant breakthroughs in mathematical reasoning capabilities. However, existing benchmarks... large language models olympiad level omni math universal https://openreview.net/forum?id=EfQv02muYG Causal Data Augmentation for Robust Fine-Tuning of Tabular Foundation Models | OpenReview Fine-tuning tabular foundation models (TFMs) in the face of scarce data is challenging, as early stopping on even scarcer validation data often fails to... tabular foundation models data augmentation fine tuning causal robust https://openreview.net/forum?id=r1lZgyBYwS HiLLoC: lossless image compression with hierarchical latent variable models | OpenReview We scale up lossless compression with latent variables, achieving state of the art on full-size ImageNet images. latent variable models lossless image compression hierarchical openreview https://openreview.net/forum?id=j4-a3SNyaY Journey to the BAOAB-limit: finding effective MCMC samplers for score-based models | OpenReview Sometimes bugs are effective MCMC samplers for score-based models. based models journey limit finding effective https://openreview.net/forum?id=1i4wNFgHDd Generalizable, real-time neural decoding with hybrid state-space models | OpenReview Real-time decoding of neural activity is central to neuroscience and neurotechnology applications, from closed-loop experiments to brain-computer interfaces,... state space models real time generalizable neural decoding https://openreview.net/forum?id=YBOQ5HnzI6 Mamba4Cast: Efficient Zero-Shot Time Series Forecasting with State Space Models | OpenReview This paper introduces Mamba4Cast, a zero-shot foundation model for time series forecasting. Based on the Mamba architecture and inspired by Prior-data Fitted... time series forecasting state space models zero shot efficient openreview https://openreview.net/forum?id=h8hy1lABh3 Towards Synthetic Data for Fine-tuning Tabular Foundation Models | OpenReview Tabular foundation models pre-trained on synthetically generated datasets have exhibited strong in-context learning capabilities. While fine-tuning can further... tabular foundation models synthetic data fine tuning towards openreview https://openreview.net/forum?id=Lm8T39vLDTE Autoregressive Diffusion Models | OpenReview We introduce Autoregressive Diffusion Models (ARDMs), a model class encompassing and generalizing order-agnostic autoregressive models (Uria et al., 2014) and... diffusion models autoregressive openreview