S3Q-Reasoning: Revealing Assumptions with Structured Scratchpads to Reduce Hallucinations in Large Language Models

The hallucination problem of large language models (LLMs) hinders their widespread application. The S3Q-Reasoning project proposes using explicit structured scratchpads to make models expose assumptions in reasoning, effectively reducing hallucinations and improving answer accuracy and interpretability. This method is lightweight and easy to implement, suitable for various scenarios.

LLM hallucinations stem from three points: 1. Lack of a fact-checking mechanism, outputting common but possibly incorrect content based on probability matching; 2. Overconfidence in generating fluent text that masks accuracy issues; 3. Implicit assumptions in reasoning are not verified. Traditional solutions like RAG and fine-tuning are either costly or complex, and S3Q-Reasoning provides a lightweight alternative.

The core idea of S3Q-Reasoning is to make models expose their thinking process (especially implicit assumptions) before generating answers, drawing on human scratchpad thinking strategies. The three-step framework: Step1 State (state key elements of the problem and known conditions); Step2 Speculate (explicitly list assumptions that the judgment depends on); Step3 Qualify (provide a preliminary judgment with confidence level and qualifying conditions).

S3Q-Reasoning is implemented through carefully designed prompt templates, requiring no additional training or model modifications. It has advantages such as zero cost, model independence, plug-and-play, and strong interpretability. A typical template guides the model to complete the three-step scratchpad (State, Speculate, Qualify) before outputting the formal answer, promoting metacognition and reducing the hallucination rate.

S3Q-Reasoning is effective in multiple scenarios: 1. Factual questions: Identify knowledge gaps and proactively suggest verification; 2. Reasoning tasks: Expose intermediate steps to find reasoning loopholes; 3. Open-ended suggestions: Reveal premises to help users evaluate applicability; 4. Code generation: Identify environmental dependencies and logical assumptions to reduce compatibility issues.

This method is not a panacea: 1. It cannot create unknown knowledge, only help admit 'I don't know'; 2. It increases output length and token consumption; 3. It relies on model cooperation (newer models like GPT-4 and Claude 3 perform better); 4. It cannot completely eliminate hallucinations; manual review is still required in critical scenarios.

Method	Cost	Effectiveness	Applicable Scenarios
S3Q-Reasoning	Low (prompt engineering)	Medium	General scenarios, rapid deployment
RAG	Medium (knowledge base maintenance)	High	Specific domains with structured knowledge sources
Fine-tuning	High (labeled data)	High	Specific tasks with large sample sizes
Multi-model verification	High (multiple times reasoning cost)	Very high	High-risk decision-making
S3Q is suitable as a baseline or combined with other methods (e.g., expose assumptions first then verify with RAG).

S3Q-Reasoning improves AI transparency, allowing users to understand the reasoning process and dependent conditions, helping to judge the credibility of answers. In today's era where AI is deeply involved in critical decisions, transparency is a necessity. It is recommended that developers try: requiring models to show scratchpads first before outputting answers to improve the reliability and interpretability of LLM outputs.