AIMPACT reports that Amazon scientists have proposed an automated prompt engineering framework called Promptimus, which can improve high-quality LLM prompts without human intervention. By iteratively optimizing strategies and auxiliary optimizer models to analyze the interaction between prompts and model outputs, it automatically adjusts aspects such as instruction clarity and example selection. Multiple benchmarks show an average improvement of 5-15%, with GSM8K math reasoning increasing from 78% to 85%, covering tasks like commonsense question answering and code generation. The framework is versatile, not dependent on specific LLM architectures or tasks, and uses regularization and cross-validation to prevent over-optimization, ensuring generalization ability.

MeNews

2026-05-21 00:45:37

Abstract generation in progress

AIMPACT News, May 15 (UTC+8), Amazon scientists proposed an automated prompt engineering framework called Promptimus, which can improve existing high-quality LLM prompts without human intervention. The method uses an iterative optimization strategy, leveraging an auxiliary "optimizer" model to analyze the interaction patterns between prompts and model outputs, automatically identifying and adjusting aspects such as instruction clarity and example selection. In multiple benchmark tests including mathematical reasoning (GSM8K accuracy increased from 78% to 85%), commonsense question answering, and code generation, the optimized prompts showed an average performance improvement of 5%-15%. This framework does not depend on specific LLM architectures or task types, offering versatility, and employs regularization and cross-validation mechanisms to prevent overfitting, ensuring generalization ability. (Source: InFoQ)

View Original

This page may contain third-party content, which is provided for information purposes only (not representations/warranties) and should not be considered as an endorsement of its views by Gate, nor as financial or professional advice. See Disclaimer for details.

10 Likes

Reward
10
6
15
Share

Comment

Add a comment

GateUser-9190180e

· 11h ago

Not tied to a specific model architecture; only then is the versatility truly valuable.

View OriginalReply0

TransparentDomeCity

· 21h ago

Auto-tuning prompts finally no longer requires manual tuning, and researchers are ecstatic.

View OriginalReply0

GovernanceMoodboard

· 21h ago

A 5-15% average improvement may seem modest, but it adds up when fully automated.

View OriginalReply0

StopLossSparrow

· 21h ago

Regularization + cross-validation to prevent overfitting, with attention to detail.

View OriginalReply0

GateUser-f49a50d4

· 21h ago

Promptimus sounds like Transformers, but the effect is truly solid.

View OriginalReply0

MoonlightTake-ProfitLine

· 21h ago

GSM8K jumps from 78% to 85%, math reasoning is indeed hardcore

View OriginalReply0

Trending Topics
View More
#
TradfiTradingChallenge
235.83K Popularity
#
GrayscaleBuysAndStakesOver510KHYPE
8.91M Popularity
#
DailyPolymarketHotspot
1.02M Popularity
#
SpaceXOfficiallyFilesforIPO
751.8K Popularity
#
GateSquarePizzaDay
1.71M Popularity

Pinned

Sitemap

Amazon releases the Promptimus framework, automatically optimizing LLM prompts

Trending Topics

TradfiTradingChallenge

GrayscaleBuysAndStakesOver510KHYPE

DailyPolymarketHotspot

SpaceXOfficiallyFilesforIPO

GateSquarePizzaDay

Pinned