Fine-Tuning and Prompt Optimization: Two Great Steps that Work Better Together

🌈 Abstract

The article discusses the problem of optimizing the performance of natural language processing (NLP) systems that are built as multi-stage pipelines involving multiple distinct language models (LMs) and prompting strategies. The key points are:

• NLP systems are increasingly taking the form of multi-stage pipelines with multiple LMs and prompting strategies.
• The authors address the challenge of how to fine-tune such systems to improve their performance.
• They cast this as a problem of optimizing the underlying LM weights and the prompting strategies together.
• They consider a realistic scenario where there are no gold labels for any intermediate stages in the pipeline.
• They evaluate approximate optimization strategies where they bootstrap training labels for all pipeline stages and use these to optimize the pipeline's prompts and fine-tune its weights alternatingly.
• Experiments on multi-hop QA, mathematical reasoning, and feature-based classification show that optimizing prompts and weights together outperforms optimizing just prompts or just weights.

🙋 Q&A

[01] Fine-Tuning and Prompt Optimization

1. What are the two key steps the article discusses for improving the performance of NLP systems built as multi-stage pipelines? The two key steps are:

• Optimizing the prompts (templates) used to invoke the language modules in the pipeline
• Fine-tuning the language model (LM) weights of the language modules

2. What is the key challenge the article addresses in optimizing these two components? The key challenge is that in realistic settings, the training set is usually very small and only a small number of LM calls are possible for training and inference. Additionally, the pipeline modules generally lack labeled outputs and exhibit sophisticated dependencies, making it difficult to optimize them.

3. How does the article propose to address this challenge? The article proposes to alternate between optimizing prompts and fine-tuning LM weights, using approximate optimization strategies where they bootstrap training labels for all pipeline stages and use these to optimize the pipeline's prompts and fine-tune its weights.

[02] Experimental Evaluation

1. What are the three datasets used in the experiments and what types of tasks do they represent? The three datasets used are:

• HotPotQA: Multi-hop reasoning
• GSM8K: Arithmetic reasoning
• Iris: Feature-based classification

2. What are the key findings from the experiments across these three datasets? The key findings are:

• In 7 out of the 9 dataset and LM pairs, the best-performing strategies are those that optimize both prompts and weights together.
• Optimizing prompts is essential on all the tasks, but optimizing prompts and weights together leads to strong gains over the best setting that only optimizes one of the two.
• The authors observe 5-78% gains for HotPotQA, 2.5-10% gains for GSM8K, and -5.9-136% gains for Iris when optimizing prompts and weights together compared to optimizing just prompts or just weights.

[03] Limitations and Future Work

1. What are the key limitations of the study mentioned by the authors? The authors mention two key limitations:

1. It is possible that other tasks, programs, or LMs could change the observed patterns in unforeseen ways.
2. The authors do not yet fully understand why both prompt optimization and fine-tuning LM weights are important for improving the performance of multi-stage LM programs.

2. What future work do the authors suggest? The authors do not explicitly suggest future work, but note that their findings could inform many researchers and practitioners interested in optimizing LM programs, and encourage them to explore optimizing prompts and fine-tuning LM weights together.

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