Summary of Sprig: Improving Large Language Model Performance by System Prompt Optimization, By Lechen Zhang et al.
SPRIG: Improving Large Language Model Performance by System Prompt Optimization
by Lechen Zhang, Tolga Ergen, Lajanugen Logeswaran, Moontae Lee, David Jurgens
First submitted to arxiv on: 18 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes SPRIG, an algorithm that iteratively constructs general system prompts for Large Language Models (LLMs) to maximize their performance in various scenarios. The authors evaluate the effectiveness of these optimized system prompts on a diverse set of tasks and find that they perform similarly to task-specific prompts. Furthermore, combining both approaches leads to improved results, highlighting their complementary nature. The study also demonstrates the generalizability of optimized system prompts across different model families, parameter sizes, and languages. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models (LLMs) are very smart computers that can do many things. But, how we ask them questions matters. So far, people have focused on making sure they’re asking the right question for a specific task. However, it’s not clear if this is the best way to get the most out of these models. This paper proposes a new approach to create general instructions that work well across many tasks. They tested this approach and found that it works just as well as customizing questions for each individual task. Moreover, combining both approaches leads to even better results. This study shows how important it is to think about the overall instructions we give these models. |
