7 citations · 15 across the 10 of their papers we have counts for
10 papers
Improving Large Language Model Planning with Action Sequence Similarity
Xinran Zhao, Hanie Sedghi, Bernd Bohnet +2
Planning is essential for artificial intelligence systems to look ahead and proactively determine a course of actions to reach objectives in the virtual and real world. Recent work…
X-Guard: Multilingual Guard Agent for Content Moderation
Bibek Upadhayay, Vahid Behzadan, Ph. D
Large Language Models (LLMs) have rapidly become integral to numerous applications in critical domains where reliability is paramount. Despite significant advances in safety framew…
Evolving Deeper LLM Thinking
Kuang-Huei Lee, Ian Fischer, Yueh-Hua Wu +4
We explore an evolutionary search strategy for scaling inference time compute in Large Language Models. The proposed approach, Mind Evolution, uses a language model to generate, re…
SFT Memorizes, RL Generalizes: A Comparative Study of Foundation Model Post-training
Tianzhe Chu, Yuexiang Zhai, Jihan Yang +6
Supervised fine-tuning (SFT) and reinforcement learning (RL) are widely used post-training techniques for foundation models. However, their roles in enhancing model generalization…
Improving Dynamic Object Interactions in Text-to-Video Generation with AI Feedback
Hiroki Furuta, Heiga Zen, Dale Schuurmans +4
Large text-to-video models hold immense potential for a wide range of downstream applications. However, they struggle to accurately depict dynamic object interactions, often result…
Autoregressive Large Language Models are Computationally Universal
Dale Schuurmans, Hanjun Dai, Francesco Zanini
We show that autoregressive decoding of a transformer-based language model can realize universal computation, without external intervention or modification of the model's weights.…
Generative Hierarchical Materials Search
Sherry Yang, Simon Batzner, Ruiqi Gao +7
Generative models trained at scale can now produce text, video, and more recently, scientific data such as crystal structures. In applications of generative approaches to materials…
Exploring and Benchmarking the Planning Capabilities of Large Language Models
Bernd Bohnet, Azade Nova, Aaron T Parisi +6
Classical and natural language planning tasks remain a difficult domain for modern large language models (LLMs). In this work, we lay the foundations for improving planning capabil…
Soft Preference Optimization: Aligning Language Models to Expert Distributions
Arsalan Sharifnassab, Saber Salehkaleybar, Sina Ghiassian +2
We propose Soft Preference Optimization (SPO), a method for aligning generative models, such as Large Language Models (LLMs), with human preferences, without the need for a reward…
Video as the New Language for Real-World Decision Making
Sherry Yang, Jacob Walker, Jack Parker-Holder +5
Both text and video data are abundant on the internet and support large-scale self-supervised learning through next token or frame prediction. However, they have not been equally l…