activity
20232025
most citedFreshLLMs: Refreshing Large Language Models with Search Engine Augmentation

7 citations · 19 across the 7 of their papers we have counts for

collaborators

7 papers

cs.SD2025

Live Music Models

Lyria Team, Antoine Caillon, Brian McWilliams +33

We introduce a new class of generative models for music called live music models that produce a continuous stream of music in real-time with synchronized user control. We release M…

cs.CL2024★ 3 cited

Rel-A.I.: An Interaction-Centered Approach To Measuring Human-LM Reliance

Kaitlyn Zhou, Jena D. Hwang, Xiang Ren +3

The ability to communicate uncertainty, risk, and limitation is crucial for the safety of large language models. However, current evaluations of these abilities rely on simple cali…

cs.CL2024★ 1 cited

Training LLMs over Neurally Compressed Text

Brian Lester, Jaehoon Lee, Alex Alemi +4

In this paper, we explore the idea of training large language models (LLMs) over highly compressed text. While standard subword tokenizers compress text by a small factor, neural t…

cs.CL2024★ 1 cited

Transfer Learning for Text Diffusion Models

Kehang Han, Kathleen Kenealy, Aditya Barua +2

In this report, we explore the potential for text diffusion to replace autoregressive (AR) decoding for the training and deployment of large language models (LLMs). We are particul…

cs.LG2023★ 7 cited

Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models

Avi Singh, John D. Co-Reyes, Rishabh Agarwal +38

Fine-tuning language models~(LMs) on human-generated data remains a prevalent practice. However, the performance of such models is often limited by the quantity and diversity of hi…

cs.CL2023★ 7 cited

FreshLLMs: Refreshing Large Language Models with Search Engine Augmentation

Tu Vu, Mohit Iyyer, Xuezhi Wang +8

Most large language models (LLMs) are trained once and never updated; thus, they lack the ability to dynamically adapt to our ever-changing world. In this work, we perform a detail…

cs.CL2023

Frontier Language Models are not Robust to Adversarial Arithmetic, or "What do I need to say so you agree 2+2=5?

C. Daniel Freeman, Laura Culp, Aaron Parisi +27

We introduce and study the problem of adversarial arithmetic, which provides a simple yet challenging testbed for language model alignment. This problem is comprised of arithmetic…