The Skeleton of Though Technique (LLMs)

The Skeleton of Thought Technique: A More Efficient Approach to LLM Text Generation

In a groundbreaking paper published at ICLR 2024, researchers introduced the "skeleton of thought" generation technique, a novel approach that mirrors human writing patterns while improving the efficiency of Large Language Model (LLM) text generation. This innovative method presents a significant departure from traditional LLM decoding techniques, offering both improved performance and reduced latency.

Understanding the Skeleton of Thought Approach

The fundamental concept behind this technique draws inspiration from human writing processes. When humans approach a writing task, they typically begin by creating a basic structure or outline before developing their complete response. This natural process has been adapted into a more efficient method for LLM text generation.

Traditional LLM approaches generate text in a single, sequential pass - you provide a topic, and the model generates the entire response at once. In contrast, the skeleton of thought technique breaks this process into parallel, structured stages, resulting in faster and more organized output.

 How the Technique Works

The process consists of three main stages:

1. Initial Skeleton Generation

The first stage involves generating a basic skeleton of the topic, typically consisting of three bullet points. Each point is kept concise, containing only 3-5 words. This creates a foundational structure for the entire piece.

 2. Initial Expansion

The skeleton points are then expanded into brief, 1-2 sentence summaries. This intermediate step helps bridge the gap between the basic structure and the final detailed content.

3. Parallel Development

The final stage involves expanding each point into detailed content. Importantly, these expansions can be processed in parallel, unlike traditional sequential generation methods. Each bullet point becomes a separate section of the final text, which are then combined into a cohesive whole.

Implementation in LangFlow

The technique has been implemented in LangFlow, making it accessible to developers and researchers. The implementation follows these steps:

1. The system takes the initial topic and generates the skeleton using a specialized prompt template.

2. Three separate parsers process the skeleton points simultaneously.

3. Each point is expanded using OpenAI-based models, generating detailed content in parallel.

4. The system combines the expanded sections into a final, comprehensive piece.

Benefits and Advantages

This approach offers several key advantages:

- Improved efficiency through parallel processing

- Reduced latency compared to traditional sequential generation

- More structured and organized output

- Better control over the content generation process

 Availability and Usage

The skeleton of thought template is now available in the LangFlow store, allowing developers to implement this technique in their own projects. This accessibility enables wider adoption and experimentation with this innovative approach to text generation.

This method represents a significant step forward in LLM text generation, combining human-inspired writing processes with efficient parallel processing to create a more effective approach to automated content generation.