12 citations · 37 across the 12 of their papers we have counts for
13 papers
LM Agents May Fail to Act on Their Own Risk Knowledge
Yuzhi Tang, Tianxiao Li, Elizabeth Li +3
Language model (LM) agents have demonstrated significant potential for automating real-world tasks, yet they pose a diverse array of potential, severe risks in safety-critical scen…
Putting It All into Context: Simplifying Agents with LCLMs
Mingjian Jiang, Yangjun Ruan, Luis Lastras +2
Recent advances in language model (LM) agents have demonstrated significant potential for automating complex real-world tasks. To make progress on these difficult tasks, LM agent a…
Reasoning to Learn from Latent Thoughts
Yangjun Ruan, Neil Band, Chris J. Maddison +1
Compute scaling for language model (LM) pretraining has outpaced the growth of human-written texts, leading to concerns that data will become the bottleneck to LM scaling. To conti…
MixMin: Finding Data Mixtures via Convex Minimization
Anvith Thudi, Evianne Rovers, Yangjun Ruan +2
Modern machine learning pipelines are increasingly combining and mixing data from diverse and disparate sources, e.g., pre-training large language models. Yet, finding the optimal…
Graph-based Uncertainty Metrics for Long-form Language Model Outputs
Mingjian Jiang, Yangjun Ruan, Prasanna Sattigeri +2
Recent advancements in Large Language Models (LLMs) have significantly improved text generation capabilities, but these systems are still known to hallucinate, and granular uncerta…
APPL: A Prompt Programming Language for Harmonious Integration of Programs and Large Language Model Prompts
Honghua Dong, Qidong Su, Yubo Gao +5
Large Language Models (LLMs) have become increasingly capable of handling diverse tasks with the aid of well-crafted prompts and integration of external tools, but as task complexi…
Observational Scaling Laws and the Predictability of Language Model Performance
Yangjun Ruan, Chris J. Maddison, Tatsunori Hashimoto
Understanding how language model performance varies with scale is critical to benchmark and algorithm development. Scaling laws are one approach to building this understanding, but…
Identifying the Risks of LM Agents with an LM-Emulated Sandbox
Yangjun Ruan, Honghua Dong, Andrew Wang +6
Recent advances in Language Model (LM) agents and tool use, exemplified by applications like ChatGPT Plugins, enable a rich set of capabilities but also amplify potential risks - s…
Weighted Ensemble Self-Supervised Learning
Yangjun Ruan, Saurabh Singh, Warren Morningstar +4
Ensembling has proven to be a powerful technique for boosting model performance, uncertainty estimation, and robustness in supervised learning. Advances in self-supervised learning…
Augment with Care: Contrastive Learning for Combinatorial Problems
Haonan Duan, Pashootan Vaezipoor, Max B. Paulus +2
Supervised learning can improve the design of state-of-the-art solvers for combinatorial problems, but labelling large numbers of combinatorial instances is often impractical due t…
Optimal Representations for Covariate Shift
Yangjun Ruan, Yann Dubois, Chris J. Maddison
Machine learning systems often experience a distribution shift between training and testing. In this paper, we introduce a simple variational objective whose optima are exactly the…
Improving Lossless Compression Rates via Monte Carlo Bits-Back Coding
Yangjun Ruan, Karen Ullrich, Daniel Severo +5
Latent variable models have been successfully applied in lossless compression with the bits-back coding algorithm. However, bits-back suffers from an increase in the bitrate equal…
Learning to Learn by Zeroth-Order Oracle
Yangjun Ruan, Yuanhao Xiong, Sashank Reddi +2
In the learning to learn (L2L) framework, we cast the design of optimization algorithms as a machine learning problem and use deep neural networks to learn the update rules. In thi…