5 citations · 10 across the 15 of their papers we have counts for
5 papers · 1 filter
Unintentional Unalignment: Likelihood Displacement in Direct Preference Optimization
Noam Razin, Sadhika Malladi, Adithya Bhaskar +3
Direct Preference Optimization (DPO) and its variants are increasingly used for aligning language models with human preferences. Although these methods are designed to teach a mode…
Networks of Networks: Complexity Class Principles Applied to Compound AI Systems Design
Jared Quincy Davis, Boris Hanin, Lingjiao Chen +3
As practitioners seek to surpass the current reliability and quality frontier of monolithic models, Compound AI Systems consisting of many language model inference calls are increa…
Bayesian Inference with Deep Weakly Nonlinear Networks
Boris Hanin, Alexander Zlokapa
We show at a physics level of rigor that Bayesian inference with a fully connected neural network and a shaped nonlinearity of the form is (perturbatively) solv…
Are More LLM Calls All You Need? Towards Scaling Laws of Compound Inference Systems
Lingjiao Chen, Jared Quincy Davis, Boris Hanin +4
Many recent state-of-the-art results in language tasks were achieved using compound systems that perform multiple Language Model (LM) calls and aggregate their responses. However,…
Principled Architecture-aware Scaling of Hyperparameters
Wuyang Chen, Junru Wu, Zhangyang Wang +1
Training a high-quality deep neural network requires choosing suitable hyperparameters, which is a non-trivial and expensive process. Current works try to automatically optimize or…