293 citations · 531 across the 4 of their papers we have counts for
6 papers
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…
Prefix-Tuning: Optimizing Continuous Prompts for Generation
Xiang Lisa Li, Percy Liang
Fine-tuning is the de facto way to leverage large pretrained language models to perform downstream tasks. However, it modifies all the language model parameters and therefore neces…
Posterior Control of Blackbox Generation
Xiang Lisa Li, Alexander M. Rush
Text generation often requires high-precision output that obeys task-specific rules. This fine-grained control is difficult to enforce with off-the-shelf deep learning models. In t…
Specializing Word Embeddings (for Parsing) by Information Bottleneck
Xiang Lisa Li, Jason Eisner
Pre-trained word embeddings like ELMo and BERT contain rich syntactic and semantic information, resulting in state-of-the-art performance on various tasks. We propose a very fast v…
A Generative Model for Punctuation in Dependency Trees
Xiang Lisa Li, Dingquan Wang, Jason Eisner
Treebanks traditionally treat punctuation marks as ordinary words, but linguists have suggested that a tree's "true" punctuation marks are not observed (Nunberg, 1990). These laten…