1.2k citations · 1.3k across the 7 of their papers we have counts for
9 papers
Follow the Wisdom of the Crowd: Effective Text Generation via Minimum Bayes Risk Decoding
Mirac Suzgun, Luke Melas-Kyriazi, Dan Jurafsky
In open-ended natural-language generation, existing text decoding methods typically struggle to produce text which is both diverse and high-quality. Greedy and beam search are know…
Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them
Mirac Suzgun, Nathan Scales, Nathanael Schärli +8
BIG-Bench (Srivastava et al., 2022) is a diverse evaluation suite that focuses on tasks believed to be beyond the capabilities of current language models. Language models have alre…
Language Models are Multilingual Chain-of-Thought Reasoners
Freda Shi, Mirac Suzgun, Markus Freitag +9
We evaluate the reasoning abilities of large language models in multilingual settings. We introduce the Multilingual Grade School Math (MGSM) benchmark, by manually translating 250…
Scaling Instruction-Finetuned Language Models
Hyung Won Chung, Le Hou, Shayne Longpre +32
Finetuning language models on a collection of datasets phrased as instructions has been shown to improve model performance and generalization to unseen tasks. In this paper we expl…
Prompt-and-Rerank: A Method for Zero-Shot and Few-Shot Arbitrary Textual Style Transfer with Small Language Models
Mirac Suzgun, Luke Melas-Kyriazi, Dan Jurafsky
We propose a method for arbitrary textual style transfer (TST)--the task of transforming a text into any given style--utilizing general-purpose pre-trained language models. Our met…
Monte Carlo Tree Search for Interpreting Stress in Natural Language
Kyle Swanson, Joy Hsu, Mirac Suzgun
Natural language processing can facilitate the analysis of a person's mental state from text they have written. Previous studies have developed models that can predict whether a pe…