Thinking structure in prompt engineering: making large language models think smarter

To truly tap the full potential of LLMs, the key is to guide them in how to think. The thinking structure in prompt engineering is the core of achieving this goal. It significantly improves the model's reasoning ability, answer reliability and transparency through carefully designed prompting technology. Today, let's learn about the thinking structure in prompt engineering.

1. Core technologies of thinking structure

1. Chain of Thought (CoT)

Thinking chain is a method to guide LLMs to reason step by step to reach the final answer. When solving problems, it imitates the human problem-solving process and is no longer limited to simple surface answers. For example, when answering the question "If there are 3 apples and 1 is given away, how many are left?", the answer of thinking chain is: "There are 3 apples at the beginning, and 1 is given away, so the number of apples left is 3 - 1 = 2". This reasoning method makes the behavior of the model easier to explain and greatly improves the logical performance in mathematics, reasoning, and multi-step problem processing. It is like building a clear reasoning path inside the model, so that each step of thinking can be traced, thereby improving the accuracy and credibility of the answer.

2. Self-Consistency Prompting

Self-consistency hinting technology allows the model to no longer rely on a single reasoning path, but to generate multiple solutions and then select the answer with the highest frequency. This method effectively avoids the model from falling into a local optimal solution or repeating errors. Taking a complex logical reasoning problem as an example, the model may think from different angles, resulting in a variety of reasoning processes and results. By comprehensively considering these diverse perspectives, the model can choose the most reasonable and consistent answer, thereby significantly improving the accuracy of the answer. This is just like when humans think about a problem, they will weigh different ideas and ultimately choose the best solution, making the model's decision more robust and reliable.

3. Verify and Edit

After the model initially generates content, verification and editing technology uses real-world data to verify and optimize the answers. In high-risk fields such as medicine and law, the accuracy of facts is crucial, and a wrong piece of information can lead to serious consequences. Through verification and editing, the model can check whether the answer is consistent with the actual situation and modify inaccurate or incomplete content. However, the effective application of this technology depends on reliable external data sources. If there are problems with the data source, irrelevant or erroneous edits may be introduced. Therefore, selecting a high-quality data source is key to ensuring the successful implementation of this technology.

4. Tree of Thoughts

The thinking tree breaks down the problem into multiple thinking steps with multiple possible branches, and evaluates multiple paths before selecting the best path. This approach provides the model with more thinking flexibility, especially for scenarios that require creative problem solving. For example, in the creative stage of designing a new product, the model can use the thinking tree to propose various design ideas from different angles, and then conduct in-depth analysis and evaluation of each idea, and finally find those non-obvious but highly innovative solutions, which greatly expands the thinking boundaries of the model.

5. Graph of Thoughts

Mind maps represent knowledge and reasoning as an interconnected network of thoughts, mimicking the way neural connections are made in the human brain. This structure can fully demonstrate its advantages in collaborative tasks and brainstorming. Multiple participants can add and improve ideas together in this network of thoughts, and different thinking paths are intertwined to form a rich system of knowledge and reasoning. By capturing multi-directional reasoning processes, mind maps can more comprehensively reflect the essence of the problem, promote the collision and integration of innovative thinking, and provide a broader perspective for solving complex problems.

6. ReAct Prompting

ReAct prompts combine reasoning and action, and the model can interact with external tools or environments while thinking. When planning a trip, the model can use external tools such as online maps and tourism databases to obtain relevant information based on user needs, and then perform reasoning and planning to generate a detailed travel plan. This approach not only increases the transparency of decision-making, but also improves the practicality and flexibility of the model, enabling the model to better adapt to various task requirements in the real world.

7. Algorithm of Thoughts (AoT)

Thinking algorithms are a framework for contextual problem solving. In the reasoning process, they use algorithms to break down complex problems. Thinking algorithms have strong advantages for structured tasks that require formal logic advancement, such as mathematical proofs and programming algorithm design. Although this method is computationally intensive, it can provide the model with rigorous reasoning steps to ensure accuracy and reliability when dealing with complex logical problems. It is a powerful tool for solving difficult problems.

8. Skeleton of Thoughts (SoT)

The mind frame technology allows the model to generate an outline first, and then fill in the details in parallel. This approach can greatly improve efficiency in scenarios that require multiple answers or multi-tasking. For example, when writing a comprehensive research report, the model can first build the outline structure of the report, including the topics and key points of each chapter, and then generate detailed content for each part at the same time, making the entire creative process more efficient and orderly, and giving full play to the model's parallel processing capabilities.

IX. Rephrase and Respond (RaR)

Rephrase and answer adjusts the question before answering it. This can significantly improve the clarity of answers to ambiguous queries. If a user asks an ambiguous question, the model can first try to reformulate the question to make it clearer before answering it. When combined with the thought chaining technique, RaR not only improves the accuracy of answers, but also digs deeper into the essence of the question, providing answers with more depth and breadth.

10. Self-Refine Prompting

Self-optimization hinting technology enables the model to evaluate its own output and continuously optimize through iteration. This technology is particularly important in autonomous systems and self-improving intelligent agents. The model can check whether there are errors or deficiencies in its own answers based on certain evaluation criteria without human intervention, and then make corrections and improvements. This not only reduces the possibility of hallucinations and errors in the model, but also enables the model to continuously improve its performance over time and achieve self-evolution.

11. Chain of Natural Language Inference (NLI)

The natural language inference chain detects and mitigates model hallucinations through a hierarchical structure. In fields such as law and medicine where information accuracy is extremely high, model hallucinations can have serious consequences. NLI technology first detects potential hallucinations in model outputs, and then corrects them through a series of reasoning steps to ensure that the information provided by the model is true and reliable, providing important guarantees for applications in key areas.

12. Chain of Verification

The verification chain uses a self-checking mechanism, where the model improves its answers by generating verification questions. This process encourages the model to think critically. After answering the questions, the model will further think about how to verify the correctness of the answers and self-check by asking relevant verification questions. This process of self-reflection and verification helps to improve the reliability of the answers, making the model's answers more rigorous and accurate.

13. Chain of Density

Density Chaining is a framework for text summarization that generates high-quality summaries by gradually adding details. Starting from a basic summary, through continuous refinement and abstraction, it ultimately generates a rich, hierarchical summary with high information density and minimal loss of meaning. When processing large amounts of text, Density Chaining is able to effectively extract key information while retaining the core content of the text, providing users with a concise and comprehensive overview of the information.

14. Chain of Dictionary

During the translation process, the dictionary chain achieves language conversion by gradually referring to multilingual dictionaries. This method is particularly important for language translation with fewer resources. It provides the model with accurate translation basis by establishing vocabulary correspondence between different languages, helping the model overcome language barriers, achieve more accurate translation, and promote cross-language communication and information dissemination.

15. Chain of Symbol

Symbolic chains replace long texts with symbols to simplify the reasoning and planning process. In tasks such as programming and logical reasoning, symbols are highly abstract and concise, which can effectively reduce cognitive load. Models can complete tasks more efficiently and improve the speed and accuracy of dealing with complex problems by operating and reasoning with symbols.

16. Chain of Explanation

Explanation chain is used to identify trigger words and targets in harmful or manipulative texts, and plays a key role in AI security and content moderation. In online conversations, timely detection and understanding of harmful intent is crucial to maintaining a good online environment. Explanation chain technology can help models analyze the semantics and context of texts, identify potentially harmful information, and take appropriate measures to protect the safety and rights of users.

17. Chain of Knowledge

In multi-source information retrieval, knowledge chain improves the accuracy and completeness of knowledge by collecting preliminary answers, evaluating various knowledge blocks and sorting their relevance. When faced with massive amounts of information, the model can use knowledge chain technology to filter out the most relevant and reliable information, provide users with high-quality knowledge services, and make information retrieval more intelligent and efficient.

18. Chain of Emotion

Emotion Chain detects and simulates emotional responses by analyzing language, which is essential for applications that require emotional understanding, such as virtual therapy assistants and social chatbots. In their interactions with users, these applications can sense the user's emotional state through emotion chain technology and give corresponding emotional responses, enhancing the user experience and making the interaction more natural and humane.

2. The importance of thinking structure in the large language model

1. Improving reasoning ability

The thinking structure enables the model to solve problems using structured logic rather than relying solely on surface patterns. When faced with complex logical reasoning, mathematical calculations, and other problems, the model can use techniques such as thinking chains and thinking algorithms to think in clear reasoning steps, deeply analyze the essence of the problem, and find accurate solutions. This logic-based reasoning method greatly improves the model's problem-solving ability and enables it to cope with a variety of challenging tasks.

2. Reduce hallucinations

Hallucination is one of the common problems of large language models when generating content, which may cause the generated information to be inconsistent with the facts. Technologies such as verification and editing in the thinking structure and natural language reasoning chain can effectively reduce the occurrence of hallucinations by comparing and verifying the output of the model with known facts or logical consistency. After the model generates content, it can use external data or reasoning rules to check and correct the answers to ensure that the output information is true and reliable, thereby improving the credibility of the model.

3. Task Specialization

Different fields and tasks have different requirements for models. The thinking structure enables the model to quickly adapt to complex tasks in specific fields without a lot of fine-tuning. In the medical field, the model can use verification and editing technology, combined with professional medical knowledge databases, to accurately answer various medical questions; in the legal field, natural language reasoning chain technology can help the model accurately interpret and apply legal provisions. By using different thinking structures, the model can perform well in various fields and achieve professional processing of tasks.

4. Simulating human cognition

The human thinking process is complex and diverse, including step-by-step reasoning, branching thinking, continuous refinement, and emotional understanding. Various techniques in the thinking structure, such as thought chains, thought trees, and emotional chains, imitate these human ways of thinking. The model can analyze problems step by step, explore solutions from different angles, and understand and respond to emotional information, just like humans, making the model's behavior closer to human cognitive patterns and improving the naturalness of interaction and user experience.

The thinking structure in the prompting project has opened up a new path for the development of large language models. Through these carefully designed prompting technologies, large language models can think, make decisions and communicate more intelligently, bringing opportunities for innovation and change to various fields. Although there are some challenges, with the continuous advancement of technology and the deepening of research, it is believed that the thinking structure will play a more important role in the future development of artificial intelligence and promote the development of large language models in a more intelligent and reliable direction.