Search Results for author:
Found 15 papers, 8 papers with code
LLM-based Cognitive Models of Students with Misconceptions
Our findings reveal that LLMs trained on misconception examples can efficiently learn to replicate errors.
MalAlgoQA: Pedagogical Evaluation of Counterfactual Reasoning in Large Language Models and Implications for AI in Education
We propose the Malgorithm Identification task, where LLMs are assessed based on their ability to identify corresponding malgorithm given an incorrect answer choice.
Synthetic Context Generation for Question Generation
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 question generation occurs.
Student Data Paradox and Curious Case of Single Student-Tutor Model: Regressive Side Effects of Training LLMs for Personalized Learning
Our findings reveal significant declines in the models' performance across these diverse benchmarks, indicating a broad impact on their capabilities when trained to model student behavior.
Marking: Visual Grading with Highlighting Errors and Annotating Missing Bits
We subsequently train language models to identify entailment, contradiction, and neutrality from student response, akin to NLI, and with the added dimension of identifying omissions from gold answers.
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.
