8 citations · 14 across the 11 of their papers we have counts for
11 papers
Learning Context: A Unified Framework and Roadmap for Context-Aware AI in Education
Naiming Liu, Brittany Bradford, Johaun Hatchett +5
We introduce a unified Learning Context (LC) framework designed to transition AI-based education from context-blind mimicry to a principled, holistic understanding of the learner.…
Synthetic Context Generation for Question Generation
Naiming Liu, Zichao Wang, Richard Baraniuk
Despite rapid advancements in large language models (LLMs), QG remains a challenging problem due to its complicated process, open-ended nature, and the diverse settings in which qu…
Novice Learner and Expert Tutor: Evaluating Math Reasoning Abilities of Large Language Models with Misconceptions
Naiming Liu, Shashank Sonkar, Zichao Wang +2
We propose novel evaluations for mathematical reasoning capabilities of Large Language Models (LLMs) based on mathematical misconceptions. Our primary approach is to simulate LLMs…
MultiQG-TI: Towards Question Generation from Multi-modal Sources
Zichao Wang, Richard Baraniuk
We study the new problem of automatic question generation (QG) from multi-modal sources containing images and texts, significantly expanding the scope of most of the existing work…
Interpretable Math Word Problem Solution Generation Via Step-by-step Planning
Mengxue Zhang, Zichao Wang, Zhichao Yang +2
Solutions to math word problems (MWPs) with step-by-step explanations are valuable, especially in education, to help students better comprehend problem-solving strategies. Most exi…
MANER: Mask Augmented Named Entity Recognition for Extreme Low-Resource Languages
Shashank Sonkar, Zichao Wang, Richard G. Baraniuk
This paper investigates the problem of Named Entity Recognition (NER) for extreme low-resource languages with only a few hundred tagged data samples. NER is a fundamental task in N…
TITAN: Bringing The Deep Image Prior to Implicit Representations
Lorenzo Luzi, Daniel LeJeune, Ali Siahkoohi +6
We study the interpolation capabilities of implicit neural representations (INRs) of images. In principle, INRs promise a number of advantages, such as continuous derivatives and a…
Retrieval-based Controllable Molecule Generation
Zichao Wang, Weili Nie, Zhuoran Qiao +3
Generating new molecules with specified chemical and biological properties via generative models has emerged as a promising direction for drug discovery. However, existing methods…
GPT-based Open-Ended Knowledge Tracing
Naiming Liu, Zichao Wang, Richard G. Baraniuk +1
In education applications, knowledge tracing refers to the problem of estimating students' time-varying concept/skill mastery level from their past responses to questions and predi…
Math Word Problem Generation with Mathematical Consistency and Problem Context Constraints
Zichao Wang, Andrew S. Lan, Richard G. Baraniuk
We study the problem of generating arithmetic math word problems (MWPs) given a math equation that specifies the mathematical computation and a context that specifies the problem s…
Enhanced Recurrent Neural Tangent Kernels for Non-Time-Series Data
Sina Alemohammad, Randall Balestriero, Zichao Wang +1
Kernels derived from deep neural networks (DNNs) in the infinite-width regime provide not only high performance in a range of machine learning tasks but also new theoretical insigh…