Ask Me Anything: A Simple Strategy for Prompting Language Models

## Purpose 
Arora et al. explore a new prompting strategy, ASK ME ANYTHING PROMPTING (AMA), to enhance the performance of Large Language Models (LLMs). This method addresses the issue of prompt brittleness – where minor changes in prompts can lead to significant performance variations in LLMs. AMA aims to simplify the process of prompt design and improve the task-transfer abilities of LLMs without additional training.

## Methods 
- Identifying effective prompt formats, particularly question-answering (QA) prompts that facilitate open-ended responses.
- Utilizing LLMs to transform task inputs into effective QA formats.
- Collecting multiple, imperfect prompts to aggregate various 'noisy' predictions for an input's true label.
- Implementing weak supervision as a strategy to combine these predictions into a final output.
- Evaluating AMA across multiple open-source model families and sizes (125M-175B parameters) on 20 popular language benchmark.

## Key Findings 
1. AMA outperforms traditional few-shot prompting methods, demonstrating an average performance lift of 10.2% over these baselines.
2. AMA enables the open-source GPT-J-6B model to match and exceed the performance of few-shot GPT3-175B on 15 of 20 benchmarks.
3. The method is effective across diverse tasks and LLM families, offering an average improvement of 41% over the 6B parameter model’s few-shot performance.
4. AMA is particularly beneficial for applications involving private data or large data operations, where using large-scale, closed-source models is challenging.

## Discussion 
AMA's approach to using multiple imperfect prompts and weak supervision is a significant advancement in the field of LLMs. It addresses the brittleness of prompts and provides a scalable and effective method for task transfer without the need for extensive model retraining. The strategy's ability to work across various models and tasks demonstrates its versatility and potential for wide application, especially in contexts where large models are impractica&#8203.

## Critiques 
1. The reliance on weak supervision could introduce complexity in understanding and diagnosing the model's decision-making process.
2. While AMA shows improvement across various models, the extent of its effectiveness in highly specialized or niche tasks remains to be fully explored.
3. The paper primarily focuses on open-source models, and further research is needed to understand AMA's applicability to proprietary or custom-built LLMs.

## Tags
#LLMs #PromptEngineering #WeakSupervision #TaskTransfer #ModelPerformance #OpenSourceModels #AIResearch.