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Summary of Optimizing Instructions and Demonstrations For Multi-stage Language Model Programs, by Krista Opsahl-ong et al.


Optimizing Instructions and Demonstrations for Multi-Stage Language Model Programs

by Krista Opsahl-Ong, Michael J Ryan, Josh Purtell, David Broman, Christopher Potts, Matei Zaharia, Omar Khattab

First submitted to arxiv on: 17 Jun 2024

Categories

  • Main: Computation and Language (cs.CL)
  • Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)

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GrooveSquid.com Paper Summaries

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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 introduces MIPRO, a novel algorithm for optimizing language model programs (LM) that consist of multiple modular calls. These LM programs are used to advance NLP tasks, but they require carefully crafted prompts to maximize performance. The authors propose prompt optimization strategies that factorize the problem into optimizing free-form instructions and few-shot demonstrations for each module. They introduce techniques such as program- and data-aware instruction proposal, stochastic mini-batch evaluation, and meta-optimization procedures to refine how LMs construct proposals over time. MIPRO outperforms baseline optimizers on five of seven diverse LM programs using the Llama-3-8B model, achieving up to 13% accuracy gains. The paper’s contributions include introducing MIPRO as a novel algorithm for optimizing LM programs and releasing the new optimizers and benchmark in DSPy. The authors demonstrate the effectiveness of MIPRO on multi-stage LM programs and its potential applications in NLP tasks.
Low GrooveSquid.com (original content) Low Difficulty Summary
This paper is about making language models (LMs) work better by giving them good instructions. LMs are like super smart computers that can do lots of things, but they need help to know what to do next. The authors came up with a new way to give LMs the right instructions so they can perform well on different tasks. They tested their method on seven different LMs and found it worked really well, making them 13% more accurate than before. This means that this new method could be used in many areas of language processing, like speech recognition or text generation. The authors shared their results with the research community so others can build upon their work.

Keywords

» Artificial intelligence  » Few shot  » Language model  » Llama  » Nlp  » Optimization  » Prompt  » Text generation