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Summary of A Study on Large Language Models’ Limitations in Multiple-choice Question Answering, by Aisha Khatun and Daniel G. Brown


A Study on Large Language Models’ Limitations in Multiple-Choice Question Answering

by Aisha Khatun, Daniel G. Brown

First submitted to arxiv on: 15 Jan 2024

Categories

  • Main: Computation and Language (cs.CL)
  • Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)

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GrooveSquid.com Paper Summaries

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Summary difficulty Written by Summary
High Paper authors High Difficulty Summary
Read the original abstract here
Medium GrooveSquid.com (original content) Medium Difficulty Summary
This paper investigates the capabilities and limitations of small Large Language Models (LLMs) used for Multiple Choice Question (MCQ) answering tasks. The analysis examines 26 open-source models, finding that most fail to understand the task or provide correct answers. Only a few models demonstrate choice order independence, raising concerns about their reliability in evaluating LLMs.
Low GrooveSquid.com (original content) Low Difficulty Summary
In simple terms, this paper looks at small language models and how well they do with multiple-choice questions. It finds that many of these models don’t really understand what they’re supposed to do or can’t pick the correct answers. Only a few models are good at it, making us wonder if we should be careful when using these models to test their abilities.

Keywords

* Artificial intelligence