Summary of Nice: to Optimize In-context Examples or Not?, by Pragya Srivastava et al.
NICE: To Optimize In-Context Examples or Not?
by Pragya Srivastava, Satvik Golechha, Amit Deshpande, Amit Sharma
First submitted to arxiv on: 9 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); 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 Recent research has demonstrated the effectiveness of in-context learning and optimization (ICE) for improving large language models’ (LLMs) performance on various tasks. However, this consensus is challenged by our study, which investigates the necessity of optimizing ICE when task-specific instructions are provided. We find that as the instruction prompt becomes more detailed, the returns on ICE optimization diminish. To characterize this behavior, we introduce a metric called Normalized Invariability to Choice of Examples (NICE), which quantifies the learnability of tasks from a given instruction. Our results suggest that NICE can reliably predict whether optimizing ICE or using random ICE is more beneficial for a new task. This study contributes to our understanding of when and how to optimize ICE, shedding light on its limitations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Have you ever wondered how artificial intelligence (AI) models learn? Recent research has shown that giving these models “hints” can make them smarter. But what if we give them more detailed hints? Our study looked at this question and found that as the hint becomes more specific, the benefits of using these hints start to fade away. We came up with a way to measure how well a model learns from a certain hint, which helps us decide whether or not to use these hints in the future. This study is important because it shows us when we can and can’t rely on these hints to improve AI models. |
Keywords
* Artificial intelligence * Optimization * Prompt
