3 citations · 4 across the 14 of their papers we have counts for
15 papers
S^3martCirc: Self-supervised Smart Circuit Discovery
Wendy Zheng, Yinhan He, Liang Wu +1
Large Language Models (LLMs) have demonstrated remarkable performance across diverse tasks, from text summarization to question answering. Despite these capabilities, their black-b…
Implicit Reasoning for Large Language Model-based Generative Recommendation
Yinhan He, Liam Collins, Bhuvesh Kumar +3
Large Language Models (LLMs) are increasingly adopted as backbones for Generative Recommendation (GR), promising access to pretrained world knowledge. Yet reliably invoking this kn…
Mult-DPO: Multinomial Direct Preference Optimization for Recommender Systems
Yaochen Zhu, Harald Steck, James McInerney +4
Direct preference optimization (DPO) is a simple and effective alignment strategy for large language models (LLMs) based on pairwise preferences. In recommender systems, however, u…
Probing to Refine: Reinforcement Distillation of LLMs via Explanatory Inversion
Zhen Tan, Chengshuai Zhao, Song Wang +3
Distilling robust reasoning capabilities from large language models (LLMs) into smaller, computationally efficient student models remains an unresolved challenge. Despite recent ad…
Saliency-Aware Multi-Route Thinking: Revisiting Vision-Language Reasoning
Mingjia Shi, Yinhan He, Yaochen Zhu +1
Vision-language models (VLMs) aim to reason by jointly leveraging visual and textual modalities. While allocating additional inference-time computation has proven effective for lar…
PhysicsAgentABM: Physics-Guided Generative Agent-Based Modeling
Kavana Venkatesh, Yinhan He, Jundong Li +1
Large language model (LLM)-based multi-agent systems enable expressive agent reasoning but are expensive to scale and poorly calibrated for timestep-aligned state-transition simula…
CausalFlip: A Benchmark for LLM Causal Judgment Beyond Semantic Matching
Yuzhe Wang, Yaochen Zhu, Jundong Li
As large language models (LLMs) witness increasing deployment in complex, high-stakes decision-making scenarios, it becomes imperative to ground their reasoning in causality rather…
IAPO: Information-Aware Policy Optimization for Token-Efficient Reasoning
Yinhan He, Yaochen Zhu, Mingjia Shi +5
Large language models increasingly rely on long chains of thought to improve accuracy, yet such gains come with substantial inference-time costs. We revisit token-efficient post-tr…
MolEdit: Knowledge Editing for Multimodal Molecule Language Models
Zhenyu Lei, Patrick Soga, Yaochen Zhu +3
Understanding and continuously refining multimodal molecular knowledge is crucial for advancing biomedicine, chemistry, and materials science. Molecule language models (MoLMs) have…
SemCoT: Accelerating Chain-of-Thought Reasoning through Semantically-Aligned Implicit Tokens
Yinhan He, Wendy Zheng, Yaochen Zhu +6
The verbosity of Chain-of-Thought (CoT) reasoning hinders its mass deployment in efficiency-critical applications. Recently, implicit CoT approaches have emerged, which encode reas…
Rank-GRPO: Training LLM-based Conversational Recommender Systems with Reinforcement Learning
Yaochen Zhu, Harald Steck, Dawen Liang +4
Large language models (LLMs) are reshaping the recommender system paradigm by enabling users to express preferences and receive recommendations through conversations. Yet, aligning…
Collaborative Retrieval for Large Language Model-based Conversational Recommender Systems
Yaochen Zhu, Chao Wan, Harald Steck +4
Conversational recommender systems (CRS) aim to provide personalized recommendations via interactive dialogues with users. While large language models (LLMs) enhance CRS with their…
Generative Risk Minimization for Out-of-Distribution Generalization on Graphs
Song Wang, Zhen Tan, Yaochen Zhu +2
Out-of-distribution (OOD) generalization on graphs aims at dealing with scenarios where the test graph distribution differs from the training graph distributions. Compared to i.i.d…
Collaborative Diffusion Model for Recommender System
Gyuseok Lee, Yaochen Zhu, Hwanjo Yu +2
Diffusion-based recommender systems (DR) have gained increasing attention for their advanced generative and denoising capabilities. However, existing DR face two central limitation…
Usable XAI: 10 Strategies Towards Exploiting Explainability in the LLM Era
Xuansheng Wu, Haiyan Zhao, Yaochen Zhu +9
Explainable AI (XAI) refers to techniques that provide human-understandable insights into the workings of AI models. Recently, the focus of XAI is being extended toward explaining…