Large Language Model
DeTeCtive: Detecting AI-generated T e xt via Multi-Level Contrastive Learning
These methodologies typically lead to performance bottlenecks and unsatisfactory generalizability.
Mixture of Demonstrations for In-Context Learning Song Wang
We further design an expert-wise training strategy to alleviate the impact of unhelpful demonstrations when optimizing the retriever model.
Language Grounded Multi-agent Reinforcement Learning with Human-interpretable Communication
Furthermore, the learned communication protocols exhibit zero-shot generalization capabilities in ad-hoc teamwork scenarios with unseen teammates and novel task states.
DAGER: Exact Gradient Inversion for Large Language Models
However, prior work has shown that the data can actually be recovered by the server using so-called gradient inversion attacks.
Embroid: Unsupervised Prediction Smoothing Can Improve Few-Shot Classification
Recent work has shown that language models' (LMs) prompt-based learning capabilities make them well suited for automating data labeling in domains where manual annotation is expensive.
MedJourney: Benchmark and Evaluation of Large Language Models over Patient Clinical Journey Xian Wu
In this paper, we address this gap by dividing a typical patient's clinical journey