Fine-Tuning and Prompt Optimization: Two Great Steps that Work Better Together
(2407.10930)Abstract
NLP systems are increasingly taking the form of multi-stage pipelines involving multiple distinct language models (LMs) and prompting strategies. Here we address the question of how to fine-tune such systems to improve their performance. We cast this as a problem of optimizing the underlying LM weights and the prompting strategies together, and consider a challenging but highly realistic scenario in which we have no gold labels for any intermediate stages in the pipeline. To address this challenge, we evaluate approximate optimization strategies in which we bootstrap training labels for all pipeline stages and use these to optimize the pipeline's prompts and fine-tune its weights alternatingly. In experiments with multi-hop QA, mathematical reasoning, and feature-based classification, we find that simple approaches for optimizing the prompts and weights together outperform directly optimizing weights alone and prompts alone by up to 65% and 5%, respectively, on average across LMs and tasks. We will release our new optimizers in DSPy at http://dspy.ai
Overview
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The paper argues for the effectiveness of combining fine-tuning and prompt optimization in multi-stage NLP pipelines to achieve superior performance.
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A new approach called the BetterTogether algorithm is proposed, which alternates between these two optimization strategies and shows significant performance improvements across various tasks and language models.
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Despite the inherent complexity and absence of intermediate labels, dual optimization is empirically proven to be beneficial, with substantial gains in tasks like question answering, mathematical reasoning, and feature-based classification.
Fine-Tuning and Prompt Optimization: Two Great Steps that Work Better Together
Introduction
The paper "Fine-Tuning and Prompt Optimization: Two Great Steps that Work Better Together" explores the intricate dynamics between fine-tuning and prompt optimization within multi-stage NLP pipelines that leverage multiple language models (LMs). Traditional NLP models often rely on either fine-tuning or prompt optimization to enhance model performance. This paper argues that combining these two strategies can yield superior results, particularly when applied iteratively.
Methodology
The authors frame the problem as jointly optimizing the underlying LM weights and prompts. This is especially challenging due to the absence of gold labels for intermediate stages in the pipeline. To address this, the paper evaluates approximate optimization strategies using a consistent bootstrapping methodology to generate training labels across all pipeline stages. The primary focus is on the BetterTogether algorithm, which alternates between prompt optimization and weight fine-tuning steps.
The methodology is tested across three tasks to ensure generalizability:
- Multi-hop Question Answering (QA) using the HotPotQA dataset.
- Mathematical reasoning using GSM8K.
- Feature-based classification using the Iris dataset.
Three distinct LMs are utilized:
- mistral-7b-instruct-v0.2
- llama-2-7b-chat
- llama-3-8b-instruct
Results
The experimental evaluation provides robust evidence supporting the benefits of combining prompt optimization with weight fine-tuning. Performance across tasks and LMs highlights significant improvements:
- HotPotQA: Accuracy gains ranged from 5% to 78%,
- GSM8K: Gains between 2.5% to 10%,
- Iris: Mixed results with decreases up to -5.9% to gains of 136%.
The results indicate that, on average, strategies that involve optimizing both prompts and weights outperform those that optimize either component in isolation. For example, for HotPotQA and the mistral-7b-instruct-v0.2 model, the accuracy improvement was as significant as 17.2% to 37.6% when alternating between prompt and weight optimization.
Discussion
This paper's findings reinforce the necessity of integrating both prompt and weight optimizations, especially in the context of NLP pipelines. This dual optimization framework is shown to yield substantial improvements across various tasks, hinting at a more universal applicability. Notably, the benefits are observed despite the inherent complexity and lack of intermediate labels in the pipeline.
Theoretical and Practical Implications
Theoretical Implications:
- This research underscores the complexity of language understanding tasks that involve multiple stages. The alternating optimization approach aligns with emerging theories on modular and compositional learning, suggesting that breaking tasks into more granular sub-tasks can yield better learning outcomes when guided by strategic optimization.
- The results challenge the conventional wisdom that fine-tuning should be the primary method for improving LM performance, highlighting the crucial role of prompt engineering as a complementary strategy.
Practical Implications:
- Practitioners can leverage the BetterTogether algorithm to enhance the efficiency and effectiveness of multi-stage NLP systems. By alternating between prompt optimization and weight fine-tuning, systems can achieve higher performance with potentially fewer computational resources.
- The release of the new optimizers in DSPy (http://dspy.ai) promises to facilitate the adoption of these methodologies in broader applications, hastening development cycles and improving the robustness of deployed NLP systems.
Future Developments in AI
The insights garnered from this paper pave the way for several intriguing future directions:
- Broader Task Applicability: Future studies should explore the efficacy of the alternating optimization approach across a wider array of NLP tasks, potentially including tasks that require higher-order reasoning or those in low-resource languages.
- Fine-Tuning Variations: Investigations into different fine-tuning strategies beyond LoRA could uncover optimized pathways that minimize the necessity for iterative prompt optimization.
- Interpretable ML: Understanding why the combination outperforms individual strategies could drive advancements in interpretable machine learning, providing clearer frameworks for the joint optimization of modular NLP systems.
Conclusion
The proposed approach of alternating between fine-tuning and prompt optimization proves to be substantially beneficial in multi-stage NLP pipelines. Empirical results substantiate that a coordinated strategy leveraging both methods can significantly outperform either in isolation. With compelling numerical results across diverse tasks and language models, this research is poised to influence the design and optimization of future NLP systems, promoting a more nuanced consideration of prompt engineering as an indispensable tool in the NLP optimization toolkit.
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