238 citations · 389 across the 7 of their papers we have counts for
7 papers
Coder Reviewer Reranking for Code Generation
Tianyi Zhang, Tao Yu, Tatsunori B. Hashimoto +4
Sampling diverse programs from a code language model and reranking with model likelihood is a popular method for code generation but it is prone to preferring degenerate solutions.…
A Closer Look at the Calibration of Differentially Private Learners
Hanlin Zhang, Xuechen Li, Prithviraj Sen +2
We systematically study the calibration of classifiers trained with differentially private stochastic gradient descent (DP-SGD) and observe miscalibration across a wide range of vi…
Data Feedback Loops: Model-driven Amplification of Dataset Biases
Rohan Taori, Tatsunori B. Hashimoto
Datasets scraped from the internet have been critical to the successes of large-scale machine learning. Yet, this very success puts the utility of future internet-derived datasets…
Diffusion-LM Improves Controllable Text Generation
Xiang Lisa Li, John Thickstun, Ishaan Gulrajani +2
Controlling the behavior of language models (LMs) without re-training is a major open problem in natural language generation. While recent works have demonstrated successes on cont…
TempLM: Distilling Language Models into Template-Based Generators
Tianyi Zhang, Mina Lee, Lisa Li +2
While pretrained language models (PLMs) have greatly improved text generation, they have also been known to produce unfaithful or inappropriate content. In contrast, classic templa…
Distributionally Robust Models with Parametric Likelihood Ratios
Paul Michel, Tatsunori Hashimoto, Graham Neubig
As machine learning models are deployed ever more broadly, it becomes increasingly important that they are not only able to perform well on their training distribution, but also yi…