---
Paper: Structured Chain-of-Thought Prompting for Code Generation
Link: https://arxiv.org/abs/2305.06599v3
---

Thank you for providing that paper on Structured Chain-of-Thought (SCoT) prompting for code generation. I'll summarize the key aspects of this technique and provide some examples.

Essence of Structured Chain-of-Thought (SCoT) Prompting:

SCoT prompting is a technique designed to enhance code generation capabilities of large language models (LLMs) by incorporating program structures into the reasoning process. The key idea is to use structured intermediate reasoning steps that mirror code structures, rather than using natural language reasoning steps as in standard Chain-of-Thought prompting.

Key ideas:
1. Uses program structures (sequence, branch, loop) to build intermediate reasoning steps
2. Includes an input-output structure to clarify requirements
3. Prompts LLMs to first generate a SCoT and then implement the final code
4. Constrains LLMs to think about requirements from a programming perspective

The method works in two steps:
1. Generate a SCoT using program structures
2. Generate code based on the SCoT

Use cases:
- Function-level code generation tasks
- Solving programming problems
- Implementing algorithms
- Translating natural language requirements into code
- Any task requiring code generation from a natural language description

Examples of SCoT Prompting:

1. Brick World task:

Requirement: Find the maximum sum of a subarray in a list of lists and divide it by K.

SCoT:
```
Input: arry: list[list], K: int
Output: result: int or float
1: Initialize a result with -999999
2: for _list in the list of lists:
3: Calculate the sum of the _list
4: if the sum is greater than result:
5: Update the result
6: Divide result by K
7: return result
```

2. Natural Language Navigation task:

Requirement: Find the nearest store from the start point given a set of roads and landmarks.

SCoT:
```
Input: roads: list[tuple], landmarks: list[tuple], start: str
Output: path: list[str]
1: Initialize a queue with the start point
2: Initialize a visited set with the start point
3: while queue is not empty:
4: Get the current location from the queue
5: if current location is a store:
6: return the path to this location
7: for each connected location:
8: if location not visited:
9: Add location to queue and visited set
10: return None if no store found
```

3. Object Counting task:

Requirement: Count the number of objects of each type in a given list.

SCoT:
```
Input: objects: list[str]
Output: count: dict[str, int]
1: Initialize an empty dictionary for count
2: for each object in objects:
3: if object is not in count:
4: Add object to count with value 1
5: else:
6: Increment count for object
7: return count
```

These examples demonstrate how SCoT prompting uses program structures to create a structured reasoning process that closely resembles the final code. This approach helps LLMs better understand the problem from a programming perspective and generate more accurate code.