7 papers
Found in Conversation: LLMs Teach Themselves to Close the Multi-Turn Gap
Tianlang Chen, Shirley Wu, Jure Leskovec
Large Language Model (LLM) interactions are typically underspecified, with users clarifying all necessary details across multiple conversational turns. Yet recent work shows that L…
Improving Diffusion Language Model Decoding through Joint Search in Generation Order and Token Space
Yangyi Shen, Tianjian Feng, Jiaqi Han +5
Diffusion Language Models (DLMs) offer order-agnostic generation that can explore many possible decoding trajectories. However, current decoding methods commit to a single trajecto…
Exchangeability in Neural Network and its Application to Dynamic Pruning
Pu, Yi, Tianlang Chen +2
Modern neural networks (NN) contain an ever-growing number of parameters, substantially increasing the memory and computational cost of inference. Researchers have explored various…
RFG: Test-Time Scaling for Diffusion Large Language Model Reasoning with Reward-Free Guidance
Tianlang Chen, Minkai Xu, Jure Leskovec +1
Diffusion large language models (dLLMs) have shown great potential in large-scale language modeling, and there is an increasing interest in further improving the capacity to solve…
Fractional Reasoning via Latent Steering Vectors Improves Inference Time Compute
Sheng Liu, Tianlang Chen, Pan Lu +4
Test-time compute has emerged as a powerful paradigm for improving the performance of large language models (LLMs), where generating multiple outputs or refining individual chains…
RelGNN: Composite Message Passing for Relational Deep Learning
Tianlang Chen, Charilaos Kanatsoulis, Jure Leskovec
Predictive tasks on relational databases are critical in real-world applications spanning e-commerce, healthcare, and social media. To address these tasks effectively, Relational D…