4 citations · 12 across the 14 of their papers we have counts for
15 papers
Optimize Your Sampling: Tuned Diffusion Sampling with Bayesian Optimization
Travis Zhang, Christian Belardi, Justin Lovelace +4
Sampling from a diffusion model typically requires many forward passes through a large neural network, making generation computationally expensive. While much work has focused on e…
Pre-training Limited Memory Language Models with Internal and External Knowledge
Linxi Zhao, Sofian Zalouk, Christian K. Belardi +7
Neural language models are black-boxes--both linguistic patterns and factual knowledge are distributed across billions of opaque parameters. This entangled encoding makes it diffic…
Learning to decode logical circuits
Yiqing Zhou, Chao Wan, Yichen Xu +3
With the development of quantum hardware bringing the error-corrected quantum circuits to the near future, the lack of an efficient polynomial-time decoding algorithms for logical…
INPROVF: Leveraging Large Language Models to Repair High-level Robot Controllers from Assumption Violations
Qian Meng, Jin Peng Zhou, Kilian Q. Weinberger +1
This paper presents INPROVF, an automatic framework that combines large language models (LLMs) and formal methods to speed up the repair process of high-level robot controllers. Pr…
Rethinking LLM Unlearning Objectives: A Gradient Perspective and Go Beyond
Qizhou Wang, Jin Peng Zhou, Zhanke Zhou +3
Large language models (LLMs) should undergo rigorous audits to identify potential risks, such as copyright and privacy infringements. Once these risks emerge, timely updates are cr…
Graders should cheat: privileged information enables expert-level automated evaluations
Jin Peng Zhou, Sébastien M. R. Arnold, Nan Ding +3
Auto-evaluating language models (LMs), i.e., using a grader LM to evaluate the candidate LM, is an appealing way to accelerate the evaluation process and the cost associated with i…
: Provably Optimal Distributional RL for LLM Post-Training
Jin Peng Zhou, Kaiwen Wang, Jonathan Chang +5
Reinforcement learning (RL) post-training is crucial for LLM alignment and reasoning, but existing policy-based methods, such as PPO and DPO, can fall short of fixing shortcuts inh…
Orchestrating LLMs with Different Personalizations
Jin Peng Zhou, Katie Z Luo, Jingwen Gu +3
This paper presents a novel approach to aligning large language models (LLMs) with individual human preferences, sometimes referred to as Reinforcement Learning from \textit{Person…
On Speeding Up Language Model Evaluation
Jin Peng Zhou, Christian K. Belardi, Ruihan Wu +4
Developing prompt-based methods with Large Language Models (LLMs) requires making numerous decisions, which give rise to a combinatorial search problem over hyper-parameters. This…
Attention to Quantum Complexity
Hyejin Kim, Yiqing Zhou, Yichen Xu +10
The imminent era of error-corrected quantum computing urgently demands robust methods to characterize complex quantum states, even from limited and noisy measurements. We introduce…
Don't Trust: Verify -- Grounding LLM Quantitative Reasoning with Autoformalization
Jin Peng Zhou, Charles Staats, Wenda Li +3
Large language models (LLM), such as Google's Minerva and OpenAI's GPT families, are becoming increasingly capable of solving mathematical quantitative reasoning problems. However,…
Detecting Out-of-Distribution Objects through Class-Conditioned Inpainting
Quang-Huy Nguyen, Jin Peng Zhou, Zhenzhen Liu +4
Recent object detectors have achieved impressive accuracy in identifying objects seen during training. However, real-world deployment often introduces novel and unexpected objects,…
Correction with Backtracking Reduces Hallucination in Summarization
Zhenzhen Liu, Chao Wan, Varsha Kishore +3
Abstractive summarization aims at generating natural language summaries of a source document that are succinct while preserving the important elements. Despite recent advances, neu…
Unsupervised Out-of-Distribution Detection with Diffusion Inpainting
Zhenzhen Liu, Jin Peng Zhou, Yufan Wang +1
Unsupervised out-of-distribution detection (OOD) seeks to identify out-of-domain data by learning only from unlabeled in-domain data. We present a novel approach for this task - Li…
Does Label Differential Privacy Prevent Label Inference Attacks?
Ruihan Wu, Jin Peng Zhou, Kilian Q. Weinberger +1
Label differential privacy (label-DP) is a popular framework for training private ML models on datasets with public features and sensitive private labels. Despite its rigorous priv…