1 citations · 2 across the 5 of their papers we have counts for
5 papers · 1 filter
Don't Repeat Yourself: Stopping Verbatim Loops at Sampling Time
Philipp Emanuel Weidmann, Allen Roush, Judah Goldfeder +2
Large Language Models generate text autoregressively, but open-ended generation is prone to verbatim looping, in which models repeat spans already present in context. Standard defe…
XTC: Head-Aware Sampling by Excluding Top Choices
Philipp Emanuel Weidmann, Allen Roush, Judah Goldfeder +2
Standard decoding rules for autoregressive language models promote diversity by rescaling the full next-token distribution or truncating its low-probability tail. These strategies…
A superpersuasive autonomous policy debating system
Allen Roush, Devin Gonier, John Hines +4
The capacity for highly complex, evidence-based, and strategically adaptive persuasion remains a formidable great challenge for artificial intelligence. Previous work, like IBM Pro…
OpenDebateEvidence: A Massive-Scale Argument Mining and Summarization Dataset
Allen Roush, Yusuf Shabazz, Arvind Balaji +7
We introduce OpenDebateEvidence, a comprehensive dataset for argument mining and summarization sourced from the American Competitive Debate community. This dataset includes over 3.…
Most Language Models can be Poets too: An AI Writing Assistant and Constrained Text Generation Studio
Allen Roush, Sanjay Basu, Akshay Moorthy +1
Despite rapid advancement in the field of Constrained Natural Language Generation, little time has been spent on exploring the potential of language models which have had their voc…