# Effect of Selection Format on LLM Performance

arXiv: https://arxiv.org/abs/2503.06926 · HTML: https://mlsanity.com/paper/2503.06926/

## 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 (most cited first, 30 of 37)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Self-Consistency Improves Chain of Thought Reasoning in Language Models](https://mlsanity.com/paper/2203.11171/) (2022)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [Tree of Thoughts: Deliberate Problem Solving with Large Language Models](https://mlsanity.com/paper/2305.10601/) (2023)
- [SciBERT: A Pretrained Language Model for Scientific Text](https://mlsanity.com/paper/1903.10676/) (2019)
- [Automatic Chain of Thought Prompting in Large Language Models](https://mlsanity.com/paper/2210.03493/) (2022)
- [A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT](https://mlsanity.com/paper/2302.11382/) (2023)
- [ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission](https://mlsanity.com/paper/1904.05342/) (2019)
- [Chain-of-Table: Evolving Tables in the Reasoning Chain for Table Understanding](https://mlsanity.com/paper/2401.04398/) (2024)
- [Enhancing Zero-Shot Chain-of-Thought Reasoning in Large Language Models through Logic](https://mlsanity.com/paper/2309.13339/) (2023)
- [TnT-LLM: Text Mining at Scale with Large Language Models](https://mlsanity.com/paper/2403.12173/) (2024)
- [MapExpert: Online HD Map Construction with Simple and Efficient Sparse Map Element Expert](https://mlsanity.com/paper/2412.12704/) (2024)
- [CM-Net: Concentric Mask based Arbitrary-Shaped Text Detection](https://mlsanity.com/paper/2011.14714/) (2020)
- [Text Growing on Leaf](https://mlsanity.com/paper/2209.03016/) (2022)
- [Challenges and Innovations in LLM-Powered Fake News Detection: A Synthesis of Approaches and Future Directions](https://mlsanity.com/paper/2502.00339/) (2025)
- [The Application of Large Language Models in Recommendation Systems](https://mlsanity.com/paper/2501.02178/) (2025)
- [Zoom Text Detector](https://mlsanity.com/paper/2209.03014/) (2022)
- [Accurate Prediction of Temperature Indicators in Eastern China Using a Multi-Scale CNN-LSTM-Attention model](https://mlsanity.com/paper/2412.07997/) (2024)
- [A Comparison of LLM Finetuning Methods & Evaluation Metrics with Travel Chatbot Use Case](https://mlsanity.com/paper/2408.03562/) (2024)
- [BiP-Net: Bidirectional Perspective Strategy based Arbitrary-Shaped Text Detection Network](https://mlsanity.com/paper/2104.04903/) (2021)
- [Enhancing disease detection in radiology reports through fine-tuning lightweight LLM on weak labels](https://mlsanity.com/paper/2409.16563/) (2024)
- [SignEye: Traffic Sign Interpretation from Vehicle First-Person View](https://mlsanity.com/paper/2411.11507/) (2024)
- [Learning the Optimal Path and DNN Partition for Collaborative Edge Inference](https://mlsanity.com/paper/2410.01857/) (2024)

## Built on by (most cited first, 3 of 3)

- [The Hidden Structure -- Improving Legal Document Understanding Through Explicit Text Formatting](https://mlsanity.com/paper/2505.12837/) (2025)
- [From 'What' to 'How' and 'Why': Sharing LLM-Generated Retrospective Summaries of Older Adults' Passive Tracking Data with Remote Family Members](https://mlsanity.com/paper/2606.03876/) (2026)
- [Isotropy Cliffs: The Geometric Signature of Decision-Making in Large Language Models](https://mlsanity.com/paper/2608.00828/) (2026)

## 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.