Nishant Balepur

2024

1. Preprint

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

   Nishant Balepur, Abhilasha Ravichander, and Rachel Rudinger

   arXiv preprint, 2024

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

   Abs Bib PDF Slides

   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.

   @article{balepur2023artifact,
     title = {Artifacts or Abduction: How Do LLMs Answer Multiple-Choice Questions Without the Question?},
     author = {Balepur, Nishant and Ravichander, Abhilasha and Rudinger, Rachel},
     journal = {arXiv preprint},
     year = {2024},
   }

2023

1. Preprint

   It’s Not Easy Being Wrong: Large Language Models Struggle with Process of Elimination Reasoning

   Nishant Balepur, Shramay Palta, and Rachel Rudinger

   arXiv preprint, 2023

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   Chain-of-thought (COT) prompting can help large language models (LLMs) reason toward correct answers, but its efficacy in reasoning toward incorrect answers is unexplored. This process of elimination (PoE), when used with COT, can enhance self-consistency, interpretability, and tasks such as medical diagnoses of exclusion. Thus, we propose PoE with COT, where LLMs must reason toward incorrect options on multiple-choice questions. We evaluate the ability of GPT-3.5, LLaMA-2, and Falcon to perform PoE with COT on a total of four commonsense and scientific reasoning datasets. We find that the strategy of PoE always underperforms the strategy of choosing the correct answer. The agreement of these strategies is also lower than the self-consistency of each strategy. To study these issues further, we conduct error analyses and give suggestions for future work.

   @article{balepur2023not,
     title = {It's Not Easy Being Wrong: Large Language Models Struggle with Process of Elimination Reasoning},
     author = {Balepur, Nishant and Palta, Shramay and Rudinger, Rachel},
     journal = {arXiv preprint},
     year = {2023},
   }

2. EMNLP 2023

   Expository Text Generation: Imitate, Retrieve, Paraphrase

   Nishant Balepur, Jie Huang, and Kevin Chen-Chuan Chang

   In The 2023 Conference on Empirical Methods in Natural Language Processing: EMNLP 2023, 2023

   Abs Bib PDF Video

   Expository documents are vital resources for conveying complex information to readers. Despite their usefulness, writing expository documents by hand is a time-consuming and labor-intensive process that requires knowledge of the domain of interest, careful content planning, and the ability to synthesize information from multiple sources. To ease these burdens, we introduce the task of expository text generation, which seeks to automatically generate an accurate and informative expository document from a knowledge source. We solve our task by developing IRP, an iterative framework that overcomes the limitations of language models and separately tackles the steps of content planning, fact selection, and rephrasing. Through experiments on three diverse datasets, we demonstrate that IRP produces high-quality expository documents that accurately inform readers.

   @inproceedings{balepur2023expository,
     title = {Expository Text Generation: Imitate, Retrieve, Paraphrase},
     author = {Balepur, Nishant and Huang, Jie and Chang, Kevin Chen-Chuan},
     booktitle = {The 2023 Conference on Empirical Methods in Natural Language Processing: EMNLP 2023},
     year = {2023},
     video = {https://youtu.be/6elNaka-JKM}
   }