𝐄𝐩𝐢𝐬𝐨𝐝𝐞 𝟏𝟑: 𝐂𝐡𝐚𝐢𝐧 𝐨𝐟 𝐓𝐚𝐬𝐤𝐬!

📅 Published on October 18, 2024

Some business processes require prompt analysis, including preliminary classification to check if all necessary information is present or to manage a sequence of intermediate tasks, just like Turing and Elektra (our two open-source AIs) do in the diagram.

I. Differences with Chain of Thought

The original concept, Chain of Thought, was designed to help models reason through problems step by step, initially applied in fields like mathematics and logic.

However, in the Chain of Tasks approach, the LLM (Large Language Model) automates task execution without focusing on reasoning, prioritizing task completion. Each step builds on the outcome of the previous one.

II. SOTA

Google: Chain of Thought (Wei et al., 2022) breaks down complex tasks into intermediate steps, enhancing logical reasoning and multi-step decision-making in LLMs. It is widely used for tasks requiring step-by-step problem-solving, improving both consistency and accuracy.

OpenAI: Tree of Thought (Yao et al., 2023) extends CoT by exploring multiple reasoning paths in parallel, evaluating different potential outcomes before selecting the best one. This approach improves decision-making by simultaneously considering various possibilities.

OpenAI: Let’s Verify Step by Step (Lightman et al., 2023) focuses on verifying each intermediate reasoning step before moving forward, ensuring that LLMs make accurate decisions. This approach strengthens reliability by catching errors at each stage.

III. Best practices for fine-tuning a model

In all cases, the output of task one becomes the input for task two. Therefore, you must carefully organize your dataset during fine-tuning. Additionally, you should ensure the following:

• Clear task definitions: Each task must be well-defined, with clear expectations for inputs and outputs.
• Data labeling: Properly labeled data is essential for enabling the LLM to understand the context and structure of the task sequence.
• Model fine-tuning: Fine-tune the LLM specifically for the types of tasks it will handle, ensuring optimal performance across multiple steps.
• Error handling: Implement mechanisms to handle errors or unexpected outputs, especially when using multi-step processes.
• Efficient resource management: Ensure your backend or agent system can efficiently manage computing resources, especially when rerunning inferences for multiple tasks.

Luckily for you, Turing and Elektra already handle this for you.