Search Results for author:
Found 10 papers, 7 papers with code
Novice Learner and Expert Tutor: Evaluating Math Reasoning Abilities of Large Language Models with Misconceptions
We propose novel evaluations for mathematical reasoning capabilities of Large Language Models (LLMs) based on mathematical misconceptions.
Code Soliloquies for Accurate Calculations in Large Language Models
Our approach notably enhances the quality of synthetic conversation datasets, especially for subjects that are calculation-intensive.
CLASS: A Design Framework for building Intelligent Tutoring Systems based on Learning Science principles
We present a design framework called Conversational Learning with Analytical Step-by-Step Strategies (CLASS) for building advanced Intelligent Tutoring Systems (ITS) powered by high-performance Large Language Models (LLMs).
TITAN: Bringing The Deep Image Prior to Implicit Representations
We study the interpolation capabilities of implicit neural representations (INRs) of images.
A Visual Tour Of Current Challenges In Multimodal Language Models
Transformer models trained on massive text corpora have become the de facto models for a wide range of natural language processing tasks.
Automated Scoring for Reading Comprehension via In-context BERT Tuning
Our approach, in-context BERT fine-tuning, produces a single shared scoring model for all items with a carefully-designed input structure to provide contextual information on each item.
NeuroView-RNN: It's About Time
A key interpretability issue with RNNs is that it is not clear how each hidden state per time step contributes to the decision-making process in a quantitative manner.
GPT-based Open-Ended Knowledge Tracing
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 predicting their future performance.
NFT-K: Non-Fungible Tangent Kernels
To further contribute interpretability with respect to classification and the layers, we develop a new network as a combination of multiple neural tangent kernels, one to model each layer of the deep neural network individually as opposed to past work which attempts to represent the entire network via a single neural tangent kernel.
Wearing a MASK: Compressed Representations of Variable-Length Sequences Using Recurrent Neural Tangent Kernels
High dimensionality poses many challenges to the use of data, from visualization and interpretation, to prediction and storage for historical preservation.
