Misinfo Reaction Frames: Reasoning about Readers’ Reactions to News Headlines

1 code implementation • ACL 2022 • Saadia Gabriel, Skyler Hallinan, Maarten Sap, Pemi Nguyen, Franziska Roesner, Eunsol Choi, Yejin Choi

Even to a simple and short news headline, readers react in a multitude of ways: cognitively (e. g. inferring the writer’s intent), emotionally (e. g. feeling distrust), and behaviorally (e. g. sharing the news with their friends).

Misinformation

STEER: Unified Style Transfer with Expert Reinforcement

1 code implementation • 13 Nov 2023 • Skyler Hallinan, Faeze Brahman, Ximing Lu, JaeHun Jung, Sean Welleck, Yejin Choi

We propose STEER: Unified Style Transfer with Expert Reinforcement, a unified frame-work developed to overcome the challenge of limited parallel data for style transfer.

Style Transfer Text Style Transfer

Tailoring Self-Rationalizers with Multi-Reward Distillation

1 code implementation • 6 Nov 2023 • Sahana Ramnath, Brihi Joshi, Skyler Hallinan, Ximing Lu, Liunian Harold Li, Aaron Chan, Jack Hessel, Yejin Choi, Xiang Ren

Results on five difficult question-answering datasets StrategyQA, QuaRel, OpenBookQA, NumerSense and QASC show that not only does MaRio improve task accuracy, but it also improves the self-rationalization quality of small LMs across the aforementioned axes better than a supervised fine-tuning (SFT) baseline.

Question Answering StrategyQA

Inference-Time Policy Adapters (IPA): Tailoring Extreme-Scale LMs without Fine-tuning

1 code implementation • 24 May 2023 • Ximing Lu, Faeze Brahman, Peter West, Jaehun Jang, Khyathi Chandu, Abhilasha Ravichander, Lianhui Qin, Prithviraj Ammanabrolu, Liwei Jiang, Sahana Ramnath, Nouha Dziri, Jillian Fisher, Bill Yuchen Lin, Skyler Hallinan, Xiang Ren, Sean Welleck, Yejin Choi

While extreme-scale language models have demonstrated exceptional performance on a variety of language tasks, the degree of control over these language models through pure prompting can often be limited.

Language Modelling reinforcement-learning +1

Self-Refine: Iterative Refinement with Self-Feedback

2 code implementations • NeurIPS 2023 • Aman Madaan, Niket Tandon, Prakhar Gupta, Skyler Hallinan, Luyu Gao, Sarah Wiegreffe, Uri Alon, Nouha Dziri, Shrimai Prabhumoye, Yiming Yang, Shashank Gupta, Bodhisattwa Prasad Majumder, Katherine Hermann, Sean Welleck, Amir Yazdanbakhsh, Peter Clark

Motivated by how humans refine their written text, we introduce Self-Refine, an approach for improving initial outputs from LLMs through iterative feedback and refinement.

Mathematical Reasoning Response Generation

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

1 code implementation • 20 Dec 2022 • Skyler Hallinan, Alisa Liu, Yejin Choi, Maarten Sap

Text detoxification has the potential to mitigate the harms of toxicity by rephrasing text to remove offensive meaning, but subtle toxicity remains challenging to tackle.

Rainier: Reinforced Knowledge Introspector for Commonsense Question Answering

1 code implementation • 6 Oct 2022 • Jiacheng Liu, Skyler Hallinan, Ximing Lu, Pengfei He, Sean Welleck, Hannaneh Hajishirzi, Yejin Choi

Our work is the first to report that knowledge generated by models that are orders of magnitude smaller than GPT-3, even without direct supervision on the knowledge itself, can exceed the quality of commonsense knowledge elicited from GPT-3.

Question Answering Reinforcement Learning (RL)

Misinfo Reaction Frames: Reasoning about Readers' Reactions to News Headlines

1 code implementation • 18 Apr 2021 • Saadia Gabriel, Skyler Hallinan, Maarten Sap, Pemi Nguyen, Franziska Roesner, Eunsol Choi, Yejin Choi

We propose Misinfo Reaction Frames (MRF), a pragmatic formalism for modeling how readers might react to a news headline.

Fact Checking Language Modelling +1