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20-Nov-24

Prompt

Are there any prompting frameworks or tools that support batch prompting in which a variable is inserted into a prompt skeleton, sent to an LLM, and then the responses are gathered and recorded?

Output

Yes, there are several prompting frameworks and tools that support batch prompting, where variables are inserted into a prompt skeleton, sent to a large language model (LLM), and the responses are gathered and recorded. These tools are designed to optimize the efficiency of LLM inference by processing multiple samples or queries in a single API call. Here are some key examples:

1. LlamaIndex (formerly GPT Index)

LlamaIndex is a framework that supports advanced prompt techniques, including variable mappings and prompt function mappings. These features allow users to define custom prompts, insert variables dynamically into prompt templates, and reuse existing prompts efficiently. While LlamaIndex focuses on creating expressive prompts, it can be extended to batch prompting scenarios by formatting multiple inputs into a single prompt template and sending them in one request. This reduces the number of API calls and improves efficiency when dealing with large volumes of data[1].

2. Batch Prompting Framework (xlang-ai)

The Batch Prompting framework is explicitly designed to support batch inference with LLMs. It allows multiple queries to be processed simultaneously in a single inference run by grouping them into batches. The framework provides tools for formatting input prompts, running batch inference, and extracting responses. This approach significantly reduces both token usage and time costs while maintaining or even improving performance across various tasks like commonsense QA, arithmetic reasoning, and more[2][3]. The framework also includes customizable configurations for datasets, prompting methods, and metrics, making it highly adaptable for different use cases.

3. HumanPrompt (HKUNLP)

The HumanPrompt project referenced by the Batch Prompting framework provides a unified interface for LLM prompting methods. It allows users to configure batch inference experiments through YAML files, where they can specify parameters such as dataset names, methods (e.g., batch inference), and API client configurations[3]. This tool is particularly useful for researchers or developers looking to benchmark batch prompting across datasets.

4. Custom Implementations for Business Applications

In real-world applications like customer service automation, businesses often implement custom batch prompting solutions by concatenating multiple queries into a single structured prompt. For example, customer queries can be combined into one input string, sent to the LLM in a single API call, and then parsed to extract individual responses for each query[4]. This approach is commonly used in industries where reducing response time and API costs is crucial.

Key Features of Batch Prompting Tools:

  • Input Formatting: Multiple queries or inputs are combined into a single structured prompt.
  • Single API Call: The combined prompt is sent to the LLM in one API call, reducing the number of requests.
  • Response Parsing: After receiving the output from the LLM, responses are extracted and matched with their respective inputs.
  • Cost Efficiency: Reduces token usage and time costs by processing multiple queries at once.

These frameworks and tools provide robust support for batch prompting scenarios where efficiency is critical, such as handling large volumes of customer queries or benchmarking across datasets.