Nishant Balepur

2025

1. Preprint

   Which of These Best Describes Multiple Choice Evaluation with LLMs? A) Forced B) Flawed C) Fixable D) All of the Above

   Nishant Balepur, Rachel Rudinger, and Jordan Lee Boyd-Graber

   2025

   Abs TL;DR Bib PDF

   Multiple choice question answering (MCQA) is popular for LLM evaluation due to its simplicity and human-like testing, but we argue for its reform. We first reveal flaws in MCQA’s format, as it struggles to: 1) test generation/subjectivity; 2) match LLM use cases; and 3) fully test knowledge. We instead advocate for generative formats based on human testing—where LLMs construct and explain answers—better capturing user needs and knowledge while remaining easy to score. We then show even when MCQA is a useful format, its datasets suffer from: leakage; unanswerability; shortcuts; and saturation. In each issue, we give fixes from education, like rubrics to guide MCQ writing; scoring methods to bridle guessing; and Item Response Theory to build harder MCQs. Lastly, we discuss LLM errors in MCQA—robustness, biases, and unfaithful explanations—showing how our prior solutions better measure or address these issues. While we do not need to desert MCQA, we encourage more efforts in refining the task based on educational testing, advancing evaluations.

   We review and critique current multiple-choice evaluation practices, and use insights from education research to propose solutions

   @article{balepur2025whose,
     title = {Which of These Best Describes Multiple Choice Evaluation with LLMs? A) Forced B) Flawed C) Fixable D) All of the Above},
     author = {Balepur, Nishant and Rudinger, Rachel and Boyd-Graber, Jordan Lee},
     year = {2025},
     tldr = {We review and critique current multiple-choice evaluation practices, and use insights from education research to propose solutions},
   }

2024

1. EMNLP 2024

   A SMART Mnemonic Sounds like “Glue Tonic”: Mixing LLMs with Student Feedback to Make Mnemonic Learning Stick

   Nishant Balepur, Matthew Shu, Alexander Hoyle, and 4 more authors

   In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, Nov 2024

   Abs TL;DR Bib PDF

   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 students prefer and aid learning.We build SMART, a mnemonic generator trained on feedback from real students learning new terms.To train SMART, we first fine-tune LLaMA-2 on a curated set of user-written mnemonics.We then use LLM alignment to enhance SMART: we deploy mnemonics generated by SMART in a flashcard app to find preferences on mnemonics students favor.We gather 2684 preferences from 45 students across two types: **expressed** (inferred from ratings) and **observed** (inferred from student learning), yielding three key findings.First, expressed and observed preferences disagree; what students *think* is helpful does not always capture what is *truly* helpful.Second, Bayesian models can synthesize complementary data from multiple preference types into a single effectiveness signal.SMART is tuned via Direct Preference Optimization on this signal, which resolves ties and missing labels in the typical method of pairwise comparisons, augmenting data for LLM output quality gains. Third, mnemonic experts assess SMART as matching GPT-4 at much lower deployment costs, showing the utility of capturing diverse student feedback to align LLMs in education.

   We generate mnemonics by aligning an LLM using preferences from 47 GRE test-takers on the mnemonics they like and which mnemonics aid learning

   @inproceedings{balepur-etal-2024-smart,
     title = {A {SMART} Mnemonic Sounds like {``}Glue Tonic{''}: Mixing {LLM}s with Student Feedback to Make Mnemonic Learning Stick},
     author = {Balepur, Nishant and Shu, Matthew and Hoyle, Alexander and Robey, Alison and Feng, Shi and Goldfarb-Tarrant, Seraphina and Boyd-Graber, Jordan Lee},
     editor = {Al-Onaizan, Yaser and Bansal, Mohit and Chen, Yun-Nung},
     booktitle = {Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing},
     month = nov,
     year = {2024},
     address = {Miami, Florida, USA},
     publisher = {Association for Computational Linguistics},
     url = {https://aclanthology.org/2024.emnlp-main.786},
     tldr = {We generate mnemonics by aligning an LLM using preferences from 47 GRE test-takers on the mnemonics they like and which mnemonics aid learning},
     pages = {14202--14225},
   }

2. ACL 2024

   Artifacts or Abduction: How Do LLMs Answer Multiple-Choice Questions Without the Question?

   Nishant Balepur, Abhilasha Ravichander, and Rachel Rudinger

   In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Aug 2024

   Best Paper Award (4%) and Oral (7%) at MASC-SSL 2024

   Abs TL;DR Bib PDF

   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 choices-only prompts, where models must select the correct answer only from the choices. In three MCQA datasets and four LLMs, this prompt bests a majority baseline in 11/12 cases, with up to 0.33 accuracy gain. To help explain this behavior, we conduct an in-depth, black-box analysis on memorization, choice dynamics, and question inference. Our key findings are threefold. First, we find no evidence that the choices-only accuracy stems from memorization alone. Second, priors over individual choices do not fully explain choices-only accuracy, hinting that LLMs use the group dynamics of choices. Third, LLMs have some ability to infer a relevant question from choices, and surprisingly can sometimes even match the original question. We hope to motivate the use of stronger baselines in MCQA benchmarks, the design of robust MCQA datasets, and further efforts to explain LLM decision-making.

   We find that LLMs don’t need the question in multiple-choice question answering to do better than random chance, and explore how

   @inproceedings{balepur-etal-2024-artifacts,
     title = {Artifacts or Abduction: How Do {LLM}s Answer Multiple-Choice Questions Without the Question?},
     author = {Balepur, Nishant and Ravichander, Abhilasha and Rudinger, Rachel},
     editor = {Ku, Lun-Wei and Martins, Andre and Srikumar, Vivek},
     booktitle = {Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
     month = aug,
     year = {2024},
     address = {Bangkok, Thailand},
     publisher = {Association for Computational Linguistics},
     url = {https://aclanthology.org/2024.acl-long.555},
     pages = {10308--10330},
     tldr = {We find that LLMs don't need the question in multiple-choice question answering to do better than random chance, and explore how}
   }