1 citations · 1 across the 5 of their papers we have counts for
5 papers
Inference-Time Conformal Reasoning with Valid Factuality Control for Large Language Models
Ting Wang, Yuanjie Shi, Yan Yan +1
Large language models (LLMs) increasingly perform multi-step reasoning, where intermediate claims form implicit directed acyclic graphs whose node correctness is structurally condi…
When to Plan, When to Polish: Noise Level as a Granularity Axis for Diffusion Language Models
Peihong Li, Yuanjie Shi, Yan Yan
Standard tokenwise diffusion LMs keep training corruption and inference commitment at token granularity throughout denoising. At high noise, this leaves scattered local fragments r…
Direct Prediction Set Minimization via Bilevel Conformal Classifier Training
Yuanjie Shi, Hooman Shahrokhi, Xuesong Jia +3
Conformal prediction (CP) is a promising uncertainty quantification framework which works as a wrapper around a black-box classifier to construct prediction sets (i.e., subset of c…
Conformal Prediction Sets for Deep Generative Models via Reduction to Conformal Regression
Hooman Shahrokhi, Devjeet Raj Roy, Yan Yan +2
We consider the problem of generating valid and small prediction sets by sampling outputs (e.g., software code and natural language text) from a black-box deep generative model for…
Deeper Insights Without Updates: The Power of In-Context Learning Over Fine-Tuning
Qingyu Yin, Xuzheng He, Luoao Deng +5
Fine-tuning and in-context learning (ICL) are two prevalent methods in imbuing large language models with task-specific knowledge. It is commonly believed that fine-tuning can surp…