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cs.CL2025

FlexOlmo: Open Language Models for Flexible Data Use

Weijia Shi, Akshita Bhagia, Kevin Farhat +20

We introduce FlexOlmo, a new class of language models (LMs) that supports (1) distributed training without data sharing, where different model parameters are independently trained…

cs.CL2025

AI as Humanity's Salieri: Quantifying Linguistic Creativity of Language Models via Systematic Attribution of Machine Text against Web Text

Ximing Lu, Melanie Sclar, Skyler Hallinan +8

Creativity has long been considered one of the most difficult aspect of human intelligence for AI to mimic. However, the rise of Large Language Models (LLMs), like ChatGPT, has rai…

cs.CL2024

WildGuard: Open One-Stop Moderation Tools for Safety Risks, Jailbreaks, and Refusals of LLMs

Seungju Han, Kavel Rao, Allyson Ettinger +5

We introduce WildGuard -- an open, light-weight moderation tool for LLM safety that achieves three goals: (1) identifying malicious intent in user prompts, (2) detecting safety ris…

cs.CL2024

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

When Hindsight is Not 20/20: Testing Limits on Reflective Thinking in Large Language Models

Yanhong Li, Chenghao Yang, Allyson Ettinger

Recent studies suggest that self-reflective prompting can significantly enhance the reasoning capabilities of Large Language Models (LLMs). However, the use of external feedback as…