activity
20202026
most citedFaith and Fate: Limits of Transformers on Compositionality

71 citations · 407 across the 69 of their papers we have counts for

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Showing 2024Show all

11 papers · 1 filter

cs.AI2024

Multi-Attribute Constraint Satisfaction via Language Model Rewriting

Ashutosh Baheti, Debanjana Chakraborty, Faeze Brahman +6

Obeying precise constraints on top of multiple external attributes is a common computational problem underlying seemingly different domains, from controlled text generation to prot…

cs.CL2024★ 2 cited

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.AI2024★ 1 cited

HAICOSYSTEM: An Ecosystem for Sandboxing Safety Risks in Human-AI Interactions

Xuhui Zhou, Hyunwoo Kim, Faeze Brahman +9

AI agents are increasingly autonomous in their interactions with human users and tools, leading to increased interactional safety risks. We present HAICOSYSTEM, a framework examini…

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

PERSONA: A Reproducible Testbed for Pluralistic Alignment

Louis Castricato, Nathan Lile, Rafael Rafailov +2

The rapid advancement of language models (LMs) necessitates robust alignment with diverse user values. However, current preference optimization approaches often fail to capture the…

cs.AI2024★ 2 cited

Certainly Uncertain: A Benchmark and Metric for Multimodal Epistemic and Aleatoric Awareness

Khyathi Raghavi Chandu, Linjie Li, Anas Awadalla +5

The ability to acknowledge the inevitable uncertainty in their knowledge and reasoning is a prerequisite for AI systems to be truly truthful and reliable. In this paper, we present…