71 citations · 130 across the 47 of their papers we have counts for
8 papers · 1 filter
Diffusion Language Models are Provably Optimal Parallel Samplers
Haozhe Jiang, Nika Haghtalab, Lijie Chen
Diffusion language models (DLMs) have emerged as a promising alternative to autoregressive models for faster inference via parallel token generation. We provide a rigorous foundati…
Sample-Adaptivity Tradeoff in On-Demand Sampling
Nika Haghtalab, Omar Montasser, Mingda Qiao
We study the tradeoff between sample complexity and round complexity in on-demand sampling, where the learning algorithm adaptively samples from distributions over a limited nu…
Panprediction: Optimal Predictions for Any Downstream Task and Loss
Sivaraman Balakrishnan, Nika Haghtalab, Daniel Hsu +2
Supervised learning is classically formulated as training a model to minimize a fixed loss function over a fixed distribution, or task. However, an emerging paradigm instead views…
On Surjectivity of Neural Networks: Can you elicit any behavior from your model?
Haozhe Jiang, Nika Haghtalab
Given a trained neural network, can any specified output be generated by some input? Equivalently, does the network correspond to a function that is surjective? In generative model…
Distortion of AI Alignment: Does Preference Optimization Optimize for Preferences?
Paul Gölz, Nika Haghtalab, Kunhe Yang
After pre-training, large language models are aligned with human preferences based on pairwise comparisons. State-of-the-art alignment methods (such as PPO-based RLHF and DPO) are…
X-Guard: Multilingual Guard Agent for Content Moderation
Bibek Upadhayay, Vahid Behzadan, Ph. D
Large Language Models (LLMs) have rapidly become integral to numerous applications in critical domains where reliability is paramount. Despite significant advances in safety framew…
From Style to Facts: Mapping the Boundaries of Knowledge Injection with Finetuning
Eric Zhao, Pranjal Awasthi, Nika Haghtalab
Finetuning provides a scalable and cost-effective means of customizing language models for specific tasks or response styles, with greater reliability than prompting or in-context…
Multi-Agent Risks from Advanced AI
Lewis Hammond, Alan Chan, Jesse Clifton +41
The rapid development of advanced AI agents and the imminent deployment of many instances of these agents will give rise to multi-agent systems of unprecedented complexity. These s…