1 citations · 3 across the 11 of their papers we have counts for
4 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…
Turning Up the Heat: Min-p Sampling for Creative and Coherent LLM Outputs
Minh Nhat Nguyen, Andrew Baker, Clement Neo +3
Large Language Models (LLMs) generate text by sampling the next token from a probability distribution over the vocabulary at each decoding step. Popular sampling methods like top-p…