263 citations · 730 across the 19 of their papers we have counts for
19 papers
Rethinking Generative Image Pretraining: How Far Are We From Scaling Up Next-Pixel Prediction?
Xinchen Yan, Chen Liang, Lijun Yu +3
This paper investigates the scaling properties of autoregressive next-pixel prediction, a simple, end-to-end yet under-explored framework for unified vision models. Starting with i…
The Bias is in the Details: An Assessment of Cognitive Bias in LLMs
R. Alexander Knipper, Charles S. Knipper, Kaiqi Zhang +3
As Large Language Models (LLMs) are increasingly embedded in real-world decision-making processes, it becomes crucial to examine the extent to which they exhibit cognitive biases.…
Type-Compliant Adaptation Cascades: Adapting Programmatic LM Workflows to Data
Chu-Cheng Lin, Daiyi Peng, Yifeng Lu +2
Reliably composing Large Language Models (LLMs) for complex, multi-step workflows remains a significant challenge. The dominant paradigm -- optimizing discrete prompts in a pipelin…
ReDiSC: A Reparameterized Masked Diffusion Model for Scalable Node Classification with Structured Predictions
Yule Li, Yifeng Lu, Zhen Wang +3
In recent years, graph neural networks (GNN) have achieved unprecedented successes in node classification tasks. Although GNNs inherently encode specific inductive biases (e.g., ac…
Seed-Free Synthetic Data Generation Framework for Instruction-Tuning LLMs: A Case Study in Thai
Parinthapat Pengpun, Can Udomcharoenchaikit, Weerayut Buaphet +1
We present a synthetic data approach for instruction-tuning large language models (LLMs) for low-resource languages in a data-efficient manner, specifically focusing on Thai. We id…
Michelangelo: Long Context Evaluations Beyond Haystacks via Latent Structure Queries
Kiran Vodrahalli, Santiago Ontanon, Nilesh Tripuraneni +21
We introduce Michelangelo: a minimal, synthetic, and unleaked long-context reasoning evaluation for large language models which is also easy to automatically score. This evaluation…