Sean Welleck
7 papers in the PaperMetrix corpus
Papers by this author
-
Dialogue Natural Language Inference
2019
Consistency is a long standing issue faced by dialogue models. In this paper, we frame the consistency of dialogue agents as natural language inference (NLI) and create a new natural language inference dataset called Dialogue …
-
Symbolic Knowledge Distillation: from General Language Models to Commonsense Models
2022 · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Peter West, Chandra Bhagavatula, Jack Hessel, Jena Hwang, Liwei Jiang, Ronan Le Bras, Ximing Lu, Sean Welleck, Yejin Choi. Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: …
-
STEER: Unified Style Transfer with Expert Reinforcement
2023 · arXiv (Cornell University)
While text style transfer has many applications across natural language processing, the core premise of transferring from a single source style is unrealistic in a real-world setting. In this work, we focus on arbitrary style …
-
Optimizing Temperature for Language Models with Multi-Sample Inference
2025 · arXiv (Cornell University)
Multi-sample aggregation strategies, such as majority voting and best-of-N sampling, are widely used in contemporary large language models (LLMs) to enhance predictive accuracy across various tasks. A key challenge in this process is temperature selection, …
-
Non-Monotonic Sequential Text Generation
2019 · arXiv (Cornell University)
Standard sequential generation methods assume a pre-specified generation order, such as text generation methods which generate words from left to right. In this work, we propose a framework for training models of text generation that …
-
Neural Text Generation with Unlikelihood Training
2019 · arXiv (Cornell University)
Neural text generation is a key tool in natural language applications, but it is well known there are major problems at its core. In particular, standard likelihood training and decoding leads to dull and repetitive …
-
Self-Refine: Iterative Refinement with Self-Feedback
2023 · arXiv (Cornell University)
Like humans, large language models (LLMs) do not always generate the best output on their first try. Motivated by how humans refine their written text, we introduce Self-Refine, an approach for improving initial outputs from …