SR-FoT: A Syllogistic-Reasoning Framework of Thought for Large Language Models Tackling Knowledge-based Reasoning Tasks (2501.11599v1)

Abstract: Deductive reasoning is a crucial logical capability that assists us in solving complex problems based on existing knowledge. Although augmented by Chain-of-Thought prompts, LLMs might not follow the correct reasoning paths. Enhancing the deductive reasoning abilities of LLMs, and leveraging their extensive built-in knowledge for various reasoning tasks, remains an open question. Attempting to mimic the human deductive reasoning paradigm, we propose a multi-stage Syllogistic-Reasoning Framework of Thought (SR-FoT) that enables LLMs to perform syllogistic deductive reasoning to handle complex knowledge-based reasoning tasks. Our SR-FoT begins by interpreting the question and then uses the interpretation and the original question to propose a suitable major premise. It proceeds by generating and answering minor premise questions in two stages to match the minor premises. Finally, it guides LLMs to use the previously generated major and minor premises to perform syllogistic deductive reasoning to derive the answer to the original question. Extensive and thorough experiments on knowledge-based reasoning tasks have demonstrated the effectiveness and advantages of our SR-FoT.

• The paper introduces SR-FoT, a multi-stage syllogistic-reasoning framework designed to improve large language models' deductive capabilities by mimicking human logical deductions.
• Experimental validation across datasets like ScienceQA and StrategyQA shows SR-FoT enhances accuracy and reasoning rigor, outperforming traditional Chain-of-Thought methods.
• SR-FoT promotes a more transparent and interpretable reasoning process crucial for applications demanding logical consistency and high-order logical proficiency in AI systems.

Analysis of SR-FoT: A Multi-Stage Syllogistic-Reasoning Framework for Enhancing Deductive Reasoning in LLMs

The paper introduces the SR-FoT, a Syllogistic-Reasoning Framework of Thought, designed to reinforce the deductive reasoning capabilities of LLMs by embedding a structured multi-stage reasoning process that mimics human logical deductions. This framework aims to bridge current gaps in LLM-based reasoning performance, which, despite advancements such as Chain-of-Thought prompting (CoT), often lacks rigor and coherence inherent to formal deductive reasoning.

Framework Structure

SR-FoT provides a systematic approach divided into five critical stages that collectively aim to enhance the deductive reasoning capabilities inherent in LLMs:

1. Question Explanation: It begins with interpreting the given question to provide a comprehensive understanding and to guide the subsequent derivation of premises. This stage lays the groundwork for targeting the appropriate lines of reasoning.
2. Major Premise Production: Utilizing the insights from the first stage, this step involves generating a major premise. It taps into the built-in knowledge of LLMs, aligning it with the context of the problem at hand.
3. Minor Premise Question Formulation: This intermediary step posits questions to uncover necessary minor premises. This tactic effectively delineates specific facts essential for applying the major premise to the original question context.
4. Minor Premise Production: Building upon the previous question, it answers using context and the LLM's inherent knowledge to formulate the necessary minor premises.
5. Final Syllogistic Reasoning: The framework culminates in an applied reasoning stage, allowing the LLM to derive conclusions by synthesizing the devised premises in a structured, logical manner.

Each stage restricts access to only pertinent information from previous stages, aligning with cognitive load reduction principles and minimizing distractions.

Experimental Validation

Extensive experimentation validated SR-FoT across several reasoning tasks derived from datasets such as ScienceQA, StrategyQA, and BoolQ. Utilizing various LLMs like GPT-3.5-turbo, DeepSeek-V2, and Qwen1.5-32B-Chat, SR-FoT demonstrated enhanced accuracy and reasoning rigor compared to traditional CoT and its variants like Self-Consistency CoT (SC-CoT) and Complexity-based CoT (C-CoT). Notable improvements were seen in ScienceQA, where SR-FoT outperformed both its predecessors and even multi-round aggregation methods.

Theoretical and Practical Implications

By incorporating syllogistic reasoning, the SR-FoT framework not only improves reasoning performance in LLMs but also instills a more transparent and interpretable reasoning process. This enhancement is crucial in applications demanding logical consistency and accuracy, such as scientific inquiries and strategic problem-solving.

Moreover, the framework's architectural principles—progressive restriction of input visibility and explicit stage-wise question formulation—can be instrumental in developing future AI systems requiring high-order logical proficiency, promoting developments in fields requiring complex decision-making or interpretability by design.

Future Directions

Given its promising results, future work could evolve SR-FoT to tackle even more complex reasoning tasks, extending its applicability to fields like legal reasoning or intricate multi-agent systems. Additionally, expanding the framework's adaptability to various LLM architectures and its integration with multi-modal inputs may further enhance its efficacy.

In conclusion, SR-FoT posits an improved methodological framework for LLM reasoning, demonstrating a viable path forward for more reliable AI systems capable of performing deductive reasoning tasks with higher degrees of accuracy and consistency.