text generation

Tuning Language Models by Proxy

We develop an algorithm for “tuning” language models at decoding-time!

Alisa Liu, Xiaochuang Han, Yizhong Wang, Yulia Tsvetkov, Yejin Choi, Noah A. Smith

Self-Instruct: Aligning Language Models with Self-Generated Instructions

Large “instruction-tuned” language models (i.e., finetuned to respond to instructions) have demonstrated a remarkable ability to …

Yizhong Wang, Yeganeh Kordi, Swaroop Mishra, Alisa Liu, Noah A. Smith, Daniel Khashabi, Hannaneh Hajishirzi

Detoxifying Text with MaRCo: Controllable Revision with Experts and Anti-Experts

Using expert and anti-expert LMs to rewrite toxic text for safety

Skyler Hallinan, Alisa Liu, Yejin Choi, Maarten Sap

WANLI: Worker and AI Collaboration for Natural Language Inference Dataset Creation

We introduce a paradigm for dataset creation based on human and machine collaboration, and demonstrate its empirical effectiveness for collecting a new large-scale NLI dataset

Alisa Liu, Swabha Swayamdipta, Noah A. Smith, Yejin Choi

Generated Knowledge Prompting for Commonsense Reasoning

Prompting GPT-3 to generate relevant background knowledge improves performance on a variety of commonsense reasoning tasks

Jiacheng Liu, Alisa Liu, Ximing Lu, Sean Welleck, Peter West, Ronan Le Bras, Yejin Choi, Hannaneh Hajishirzi

DExperts: Decoding-Time Controlled Text Generation with Experts and Anti-Experts

Steering open-ended text generation toward desired or away from undesired attributes, using expert and anti-expert language models

Alisa Liu, Maarten Sap, Ximing Lu, Swabha Swayamdipta, Chandra Bhagavatula, Noah A. Smith, Yejin Choi