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20212023
most citedThe Generative AI Paradox: "What It Can Create, It May Not Understand"

10 citations · 12 across the 5 of their papers we have counts for

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6 papers · 1 filter

cs.CL2024

StyleRemix: Interpretable Authorship Obfuscation via Distillation and Perturbation of Style Elements

Jillian Fisher, Skyler Hallinan, Ximing Lu +3

Authorship obfuscation, rewriting a text to intentionally obscure the identity of the author, is an important but challenging task. Current methods using large language models (LLM…

cs.CL2024

How to Train Your Fact Verifier: Knowledge Transfer with Multimodal Open Models

Jaeyoung Lee, Ximing Lu, Jack Hessel +5

Given the growing influx of misinformation across news and social media, there is a critical need for systems that can provide effective real-time verification of news claims. Larg…

cs.CL20241 cited

WildTeaming at Scale: From In-the-Wild Jailbreaks to (Adversarially) Safer Language Models

Liwei Jiang, Kavel Rao, Seungju Han +8

We introduce WildTeaming, an automatic LLM safety red-teaming framework that mines in-the-wild user-chatbot interactions to discover 5.7K unique clusters of novel jailbreak tactics…

cs.CL2024

JAMDEC: Unsupervised Authorship Obfuscation using Constrained Decoding over Small Language Models

Jillian Fisher, Ximing Lu, Jaehun Jung +3

The permanence of online content combined with the enhanced authorship identification techniques calls for stronger computational methods to protect the identity and privacy of onl…

cs.CL2023

STEER: Unified Style Transfer with Expert Reinforcement

Skyler Hallinan, Faeze Brahman, Ximing Lu +3

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 setti…

cs.CL20212 cited

NeuroLogic A*esque Decoding: Constrained Text Generation with Lookahead Heuristics

Ximing Lu, Sean Welleck, Peter West +9

The dominant paradigm for neural text generation is left-to-right decoding from autoregressive language models. Constrained or controllable generation under complex lexical constra…