Effect of Selection Format on LLM Performance
Fuente:
arXiv
Salvato in:
| Autori principali: | , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866909651185958912 |
|---|---|
| author | Han, Yuchen Wu, Yucheng Willard, Jeffrey |
| author_facet | Han, Yuchen Wu, Yucheng Willard, Jeffrey |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_06926 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Effect of Selection Format on LLM Performance Han, Yuchen Wu, Yucheng Willard, Jeffrey Computation and Language Artificial Intelligence Computational Engineering, Finance, and Science Emerging Technologies Machine Learning 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. |
| title | Effect of Selection Format on LLM Performance |
| topic | Computation and Language Artificial Intelligence Computational Engineering, Finance, and Science Emerging Technologies Machine Learning |
| url | https://arxiv.org/abs/2503.06926 |