Small fixes to prompts improve accuracy and shorten them
A study of seven language-processing tests shows that grouped error fixes can improve average accuracy and shorten prompts, but results depend on task and data stability.
- Publication
- arXiv
- Stage
- Preprint
- What we read
- Summary of the paper
- Authors
- Lihao Liu, Peng Tang, Kunwar Yashraj Singh, Shabnam Ghadar
- Universities and research institutions
- Not yet supplied in verified metadata; the Brief does not guess.
Institution metadata: OpenAlex record ↗
What they did and found
Seven language-processing tests started from weak prompts. A method that groups errors, builds several focused prompts, and uses bootstrap voting to pick the best one. On average, accuracy rose and prompts shortened, with variation by model and task.
Why it matters
Shorter, reliable prompts can save time and money in AI work; expect some variation and check results on new data.
An error map identifies common prompt problems, then creates a few concise prompts that target those causes. This keeps prompts short while aiming for steady results.
In real teams, updating customer prompts could use this approach to balance clearer rules with steadier performance across different user inputs.
Original sources · 1
- ESPO: Error-Structured Prompt Optimization via Diagnose, Diversify, and Stabilize ↗arXiv · 2026-09-03
Check the original paper for its authors, methods, version and access terms.
What remains uncertain
Results depend on how errors are grouped and tested; gains vary by task and model.
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