8 citations · 19 across the 4 of their papers we have counts for
4 papers
Predicting vs. Acting: A Trade-off Between World Modeling & Agent Modeling
Margaret Li, Weijia Shi, Artidoro Pagnoni +2
RLHF-aligned LMs have shown unprecedented ability on both benchmarks and long-form text generation, yet they struggle with one foundational task: next-token prediction. As RLHF mod…
Trusting Your Evidence: Hallucinate Less with Context-aware Decoding
Weijia Shi, Xiaochuang Han, Mike Lewis +3
Language models (LMs) often struggle to pay enough attention to the input context, and generate texts that are unfaithful or contain hallucinations. To mitigate this issue, we pres…
Scaling Expert Language Models with Unsupervised Domain Discovery
Suchin Gururangan, Margaret Li, Mike Lewis +4
Large language models are typically trained densely: all parameters are updated with respect to all inputs. This requires synchronization of billions of parameters across thousands…
NN-Adapter: Efficient Domain Adaptation for Black-Box Language Models
Yangsibo Huang, Daogao Liu, Zexuan Zhong +2
Fine-tuning a language model on a new domain is standard practice for domain adaptation. However, it can be infeasible when it comes to modern large-scale language models such as G…