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LLMs Get a Boost in Prompt Robustness with Prompt-Agnostic Fine-Tuning (PAFT)

Large language models (LLMs) are revolutionizing natural language processing, but their performance can be surprisingly fragile. Even slight changes to the phrasing of a prompt can significantly impact their accuracy. A new technique, called Prompt-Agnostic Fine-Tuning (PAFT), promises to significantly improve the robustness of LLMs, making them less sensitive to variations in how users phrase their requests.

The core problem, as highlighted in the research paper “PAFT: Prompt-Agnostic Fine-Tuning,” by Wei et al., lies in the way LLMs are typically fine-tuned for specific downstream tasks. Traditional supervised fine-tuning (SFT) uses a fixed set of prompts during training. This leads to overfitting: the model learns to associate specific outputs with very specific prompt formulations, rather than learning the underlying principles of the task. As a result, slight deviations in wording during inference can cause significant performance drops – sometimes even to the level of random guessing. For example, Figure 1 in the paper demonstrates that changing a single word in a question’s prompt can lead to an accuracy drop from 86.27% to 66.93%.

PAFT addresses this problem by dynamically adjusting prompts during the fine-tuning phase. Instead of relying on a static set of prompts, PAFT generates a diverse pool of carefully constructed, synthetic prompts covering a wide range of phrasing and styles. These prompts are then randomly sampled and used during training. This process forces the model to learn the underlying principles of the task, making it less reliant on the exact wording of any single prompt.

The researchers used a two-stage process:

  1. Candidate Prompt Construction: They generated a wide array of prompts using multiple commercial LLMs, both with and without the assistance of example prompts. This ensures diverse and high-quality prompts to expose the model to various formulations.

  2. Dynamic Fine-Tuning: During training, prompts were randomly selected from the diverse pool, creating dynamic training inputs. This dynamic exposure helps the model generalize better and become less sensitive to specific prompt variations.

Extensive experiments across various datasets and LLMs demonstrated that PAFT significantly improves both prompt robustness and generalization. Models trained with PAFT consistently outperformed those trained with traditional SFT, achieving higher accuracy and significantly lower variance across a wide range of prompts, including entirely unseen ones (see Figure 4). Furthermore, PAFT didn’t negatively impact training efficiency, and in some cases even led to slightly faster inference times.

The researchers performed ablation studies to examine the effect of key parameters like the number of prompts and training epochs. They found that PAFT is quite robust to changes in these parameters, and even with a relatively small number of prompts still significantly outperforms SFT-trained models.

While the research demonstrates the significant promise of PAFT, future work could explore more sophisticated prompt sampling strategies to further improve efficiency and robustness. Additionally, exploring techniques like adversarial training could further enhance the resilience of models against unexpected prompt variations.

This work presents a crucial step forward in making LLMs more robust and reliable in real-world applications where users will inevitably present their queries in various styles and phrasing. By reducing the sensitivity to exact prompt wording, PAFT brings us closer to truly user-friendly and adaptable AI systems.