mlsanityData to 14 Oct 2026

Effect of Selection Format on LLM Performance

arXiv 2503.06926 ↗First posted 10 Mar 2025cs.CL cs.AI cs.CEcited by 3

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Abstract (arXiv)

This paper investigates a critical aspect of large language model (LLM) performance: the optimal formatting of classification task options in prompts. Through an extensive experimental study, we compared two selection formats -- bullet points and plain English -- to determine their impact on model performance. Our findings suggest that presenting options via bullet points generally yields better results, although there are some exceptions. Furthermore, our research highlights the need for continued exploration of option formatting to drive further improvements in model performance.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 37 of its 78 references that are in the map. First posted on arXiv 10 Mar 2025.

Builds on

37 of 78 references.

References · 37

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25 more
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 2.

3 papers.

  1. 2025The Hidden Structure -- Improving Legal Document Understanding Through Explicit Text Formattingcited by 5
  2. 2026From 'What' to 'How' and 'Why': Sharing LLM-Generated Retrospective Summaries of Older Adults' Passive Tracking Data with Remote Family Memberscited by 0
  3. 2026Isotropy Cliffs: The Geometric Signature of Decision-Making in Large Language Modelscited by 0