Papers by Skyler Hallinan
Inference-Time Policy Adapters (IPA): Tailoring Extreme-Scale LMs without Fine-tuning (2023.emnlp-main)
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Ximing Lu, Faeze Brahman, Peter West, Jaehun Jung, Khyathi Chandu, Abhilasha Ravichander, Prithviraj Ammanabrolu, Liwei Jiang, Sahana Ramnath, Nouha Dziri, Jillian Fisher, Bill Lin, Skyler Hallinan, Lianhui Qin, Xiang Ren, Sean Welleck, Yejin Choi
| Challenge: | Extreme-scale language models have shown exceptional performance on a variety of language tasks, but the degree of control offered by these models through pure prompting is limited. |
| Approach: | They propose an inference-time policy adapter which tailors a large base model without fine-tuning it. |
| Outcome: | The proposed model outperforms baseline methods on five challenging text generation tasks and even over GPT-4. |
Misinfo Reaction Frames: Reasoning about Readers’ Reactions to News Headlines (2022.acl-long)
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Saadia Gabriel, Skyler Hallinan, Maarten Sap, Pemi Nguyen, Franziska Roesner, Eunsol Choi, Yejin Choi
| Challenge: | Empirical results confirm that it is indeed possible for neural models to predict the prominent patterns of readers’ reactions to previously unseen news headlines. |
| Approach: | They propose a pragmatic formalism for modeling how readers might react to a news headline . they propose 'misinfo' frames, which can be used to model reader perceptions of news reliability . |
| Outcome: | The proposed model can predict readers' reactions to previously unseen headlines. |
Detoxifying Text with MaRCo: Controllable Revision with Experts and Anti-Experts (2023.acl-short)
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| Challenge: | Text detoxification can mitigate the harms of toxicity by rephrasing text to remove offensive meaning, but subtle toxicity remains challenging to tackle. |
| Approach: | They propose a text detoxification algorithm that combines controllable generation and text rewriting methods using a Product of Experts and autoencoder language models to find candidate words to mask and potentially replace. |
| Outcome: | The proposed method outperforms baselines on automatic metrics and is preferred 2.1 times more in human evaluation. |
Amulet: Putting Complex Multi-Turn Conversations on the Stand with LLM Juries (2025.emnlp-main)
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| Challenge: | a typical human-assistant conversation is lengthy and shows significant diversity in topics, intents, and requirements across turns. |
| Approach: | They propose a framework that leverages pertinent linguistic concepts of dialog-acts and maxims to improve the accuracy of LLM-judges on preference data with complex, multi-turn conversational context. |
| Outcome: | The proposed framework improves on 4 challenging datasets showing that humans frequently change their intents from one turn of the conversation to the next. |
StyleRemix: Interpretable Authorship Obfuscation via Distillation and Perturbation of Style Elements (2024.emnlp-main)
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| Challenge: | Authorship obfuscation methods that ignore author-specific stylistic features are often too rigid and lead to degradation of fluency and grammaticality. |
| Approach: | They propose an adaptive obfuscation method that perturbs stylistic elements of text . authors release a large set of 30K high-quality, long-form texts from a diverse set of 14 authors . |
| Outcome: | The proposed method outperforms state-of-the-art methods on an array of domains on automatic and human evaluation. |
STEER: Unified Style Transfer with Expert Reinforcement (2023.findings-emnlp)
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| Challenge: | Experimental results show unified style transfer models outperform the 175B instruction-tuned GPT-3 on overall style transfer quality. |
| Approach: | They propose a unified style transfer framework that can transfer to multiple target styles from an arbitrary source style. |
| Outcome: | The proposed method outperforms the 175B instruction-tuned GPT-3 on overall style transfer quality despite being 226 times smaller in size . |
Rainier: Reinforced Knowledge Introspector for Commonsense Question Answering (2022.emnlp-main)
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| Challenge: | Recent research shows that relevant knowledge can provide useful context for commonsense tasks. |
| Approach: | They propose a method that learns to generate contextually relevant knowledge in response to given questions. |
| Outcome: | The proposed method shows consistent gains over 9 commonsense benchmarks. |