14 papers
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…
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…
Wired for Overconfidence: A Mechanistic Perspective on Inflated Verbalized Confidence in LLMs
Tianyi Zhao, Yinhan He, Wendy Zheng +2
Large language models are often not just wrong, but \emph{confidently wrong}: when they produce factually incorrect answers, they tend to verbalize overly high confidence rather th…
Reforming the Mechanism: Editing Reasoning Patterns in LLMs with Circuit Reshaping
Zhenyu Lei, Qiong Wu, Jianxiong Dong +4
Large language models (LLMs) often exhibit flawed reasoning ability that undermines reliability. Existing approaches to improving reasoning typically treat it as a general and mono…
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…