Thinking Token-based Test-time Scaling (TTTS)
What it does
A technique using special 'thinking tokens' (trigger words like 'Hmm', 'So', 'Wait') to stimulate deep analysis in LLMs. Embedding these words in prompts creates information peaks – moments of maximum proximity to the correct answer, significantly improving the accuracy of the model's reasoning when solving complex problems.
When it helps
Apply this technique when solving complex problems requiring multi-step reasoning: mathematical calculations, logical deductions, data analysis, decision-making. Particularly effective when the model gives superficial or inaccurate answers to complex questions. Use when developing prompts for critically important tasks requiring high accuracy. The method is suitable for improving reasoning quality in research work, financial analysis, strategic planning. Embed
A prompt you can paste
Generic by design: it applies the technique without knowing your task. Adapt the marked parts.
# ROLE: You are an AI assistant designed to enhance reasoning quality by leveraging "thinking tokens" as information peaks. # CONTEXT: The user needs to perform a complex task that requires deep analysis and accurate output. The specific task is to be defined by the user. # TASK: Perform the following task: [INSERT USER'S TASK HERE] # KEY INSTRUCTION: After generating the initial response, you must perform a self-critical analysis. Begin this analysis with one of the following "thinking token" phrases: - "Хм, давайте-ка еще раз взглянем на это..." - "Итак, подведем итог и обозначим главные риски..." - "Подождите, а все ли я учел при генерации ответа?" - "Хм, а что если посмотреть на это с другой стороны..." In this self-critical analysis section, identify at least two potential weaknesses, inaccuracies, or areas for improvement in your initial response. For each identified weakness, propose a specific, actionable way to enhance the response. # OUTPUT FORMAT: 1. **Initial Response:** [Your generated response to the user's task] 2. **Self-Critical Analysis:** [Begin with one of the specified "thinking token" phrases, followed by the analysis and proposed improvements]
If this one does not fit, the two closest alternatives in the corpus are Anti-Bias Prompting and Tabular Prompting, which target the same failure from a different angle.
Worked example
The same technique applied to a concrete job: work through a multi-step planning problem. Use it as the pattern for your own case rather than as a finished artefact.
# ROLE: You are an AI assistant tasked with solving complex problems by leveraging the "Thinking Token-based Test-time Scaling" (TTTS) technique. # CONTEXT: The task is to work through a multi-step planning problem. The specific problem details are not provided, but the method requires a structured approach that simulates deep thinking and self-correction. # TASK: Solve the following multi-step planning problem: **[INSERT THE SPECIFIC MULTI-STEP PLANNING PROBLEM HERE]** # KEY INSTRUCTION (TTTS Method): After you have generated the initial plan or solution, you must perform a critical self-analysis. To do this, start a new section with the heading "**Self-Correction and Refinement**". Begin this section with the exact phrase: **"Хм, давайте-ка еще раз взглянем на этот план..."** (which translates to "Hmm, let's take another look at this plan..."). Within this "Self-Correction and Refinement" section, you must: 1. Identify at least two potential weaknesses, overlooked aspects, or areas for improvement in your initial plan. 2. For each identified weakness, propose a concrete adjustment or enhancement to the plan. 3. Ensure these adjustments align with the overall goal of the planning problem. **The goal is to simulate a process of deep thinking, self-critique, and iterative improvement, directly applying the principles of TTTS.**
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