97 citations · 102 across the 24 of their papers we have counts for
7 papers · 1 filter
Plausibly Problematic Questions in Multiple-Choice Benchmarks for Commonsense Reasoning
Shramay Palta, Nishant Balepur, Peter Rankel +3
Questions involving commonsense reasoning about everyday situations often admit many or answers. In contrast, multiple-choice question (MCQ…
Reverse Question Answering: Can an LLM Write a Question so Hard (or Bad) that it Can't Answer?
Nishant Balepur, Feng Gu, Abhilasha Ravichander +3
Question answering (QA), giving correct answers to questions, is a popular task, but we test reverse question answering (RQA): for an input answer, give a question with that answer…
Is Your Large Language Model Knowledgeable or a Choices-Only Cheater?
Nishant Balepur, Rachel Rudinger
Recent work shows that large language models (LLMs) can answer multiple-choice questions using only the choices, but does this mean that MCQA leaderboard rankings of LLMs are large…
A SMART Mnemonic Sounds like "Glue Tonic": Mixing LLMs with Student Feedback to Make Mnemonic Learning Stick
Nishant Balepur, Matthew Shu, Alexander Hoyle +4
Keyword mnemonics are memorable explanations that link new terms to simpler keywords. Prior work generates mnemonics for students, but they do not train models using mnemonics stud…
The Prompt Report: A Systematic Survey of Prompt Engineering Techniques
Sander Schulhoff, Michael Ilie, Nishant Balepur +28
Generative Artificial Intelligence (GenAI) systems are increasingly being deployed across diverse industries and research domains. Developers and end-users interact with these syst…
Artifacts or Abduction: How Do LLMs Answer Multiple-Choice Questions Without the Question?
Nishant Balepur, Abhilasha Ravichander, Rachel Rudinger
Multiple-choice question answering (MCQA) is often used to evaluate large language models (LLMs). To see if MCQA assesses LLMs as intended, we probe if LLMs can perform MCQA with c…
KARL: Knowledge-Aware Retrieval and Representations aid Retention and Learning in Students
Matthew Shu, Nishant Balepur, Shi Feng +1
Flashcard schedulers rely on 1) student models to predict the flashcards a student knows; and 2) teaching policies to pick which cards to show next via these predictions. Prior stu…