285 citations · 338 across the 49 of their papers we have counts for
9 papers · 1 filter
Diffusion Language Model Parallel Decoding via Product-of-Experts Bridge
Juntong Shi, Brian L. Trippe, Jure Leskovec +2
Diffusion language models (DLMs) offer substantial speed advantages through parallel decoding, but the lack of token dependencies limits generation quality compared to autoregressi…
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…
HumanLM: Simulating Users with State Alignment Beats Response Imitation
Shirley Wu, Evelyn Choi, Arpandeep Khatua +7
Large Language Models (LLMs) are increasingly used to simulate how specific users respond to a given context, enabling more user-centric applications that rely on user feedback. Ho…
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…
RFG: Self-Improving Diffusion Large Language Models with Reward-Free Guidance
Tianlang Chen, Minkai Xu, Jure Leskovec +1
Diffusion Large Language Models (dLLMs) have shown strong reasoning capabilities, yet further improving them typically requires costly post-training with additional data and superv…
MIRIAD: Augmenting LLMs with millions of medical query-response pairs
Qinyue Zheng, Salman Abdullah, Sam Rawal +7
LLMs are bound to transform healthcare with advanced decision support and flexible chat assistants. However, LLMs are prone to generate inaccurate medical content. To ground LLMs i…
Large Language Models are Good Relational Learners
Fang Wu, Vijay Prakash Dwivedi, Jure Leskovec
Large language models (LLMs) have demonstrated remarkable capabilities across various domains, yet their application to relational deep learning (RDL) remains underexplored. Existi…
-PO: Generalizing Preference Optimization with -divergence Minimization
Jiaqi Han, Mingjian Jiang, Yuxuan Song +2
Preference optimization has made significant progress recently, with numerous methods developed to align language models with human preferences. This paper introduces -divergenc…
Energy-Based Diffusion Language Models for Text Generation
Minkai Xu, Tomas Geffner, Karsten Kreis +5
Despite remarkable progress in autoregressive language models, alternative generative paradigms beyond left-to-right generation are still being actively explored. Discrete diffusio…