Judgment-of-Thought Prompting: A Courtroom-Inspired Framework for Binary Logical Reasoning with Large Language Models

Abstract: This paper proposes a novel prompting approach, Judgment of Thought (JoT), specifically tailored for binary logical reasoning tasks. Despite advances in prompt engineering, existing approaches still face limitations in handling complex logical reasoning tasks. To address these issues, JoT introduces a multi-agent approach with three specialized roles$\unicode{x2010}$$\unicode{x2010}$$\unicode{x2010}$lawyer, prosecutor, and judge$\unicode{x2010}$$\unicode{x2010}$$\unicode{x2010}$where a high-level model acts as the judge, and lower-level models serve as lawyer and prosecutor to systematically debate and evaluate arguments. Experimental evaluations on benchmarks such as BigBenchHard and Winogrande demonstrate JoT's superior performance compared to existing prompting approaches, achieving notable improvements, including 98\% accuracy in Boolean expressions. Also, our ablation studies validate the critical contribution of each role, iterative refinement loops, and feedback mechanisms. Consequently, JoT significantly enhances accuracy, reliability, and consistency in binary reasoning tasks and shows potential for practical applications.

• The paper introduces Judgment of Thought (JoT) as a novel role-based prompt engineering technique that enhances binary logical reasoning in LLMs.
• It employs distinct roles—lawyer, prosecutor, and judge—to iteratively refine arguments and improve performance on tasks like fake news detection.
• JoT outperforms traditional methods with up to 96% accuracy and 0.97 F1 score, indicating strong potential for real-world applications.

Judgment of Thoughts: A New Approach to Binary Logical Reasoning in LLMs

The paper "Judgment of Thoughts: Courtroom of the Binary Logical Reasoning in LLMs" presents a novel prompt engineering technique, termed Judgment of Thought (JoT), that aims to enhance binary logical reasoning tasks in LLMs. The JoT method deploys a role-based framework using three distinct roles—lawyer, prosecutor, and judge. This configuration is designed to facilitate more accurate and reliable reasoning in LLMs by leveraging different model levels for distinct roles, with high-level models serving as judges and lower-level ones acting as lawyers and prosecutors. Experimental evaluations indicate that JoT outperforms existing techniques like Chain of Thought (CoT) and Self-Consistency (SC) in binary logical reasoning and shows promising results in real-world tasks like Fake News Detection and SMS Spam Detection.

Methodology: Judgment of Thought (JoT)

The unique aspect of JoT lies in its role-based architecture. By structuring the prompt engineering process to simulate a courtroom scenario, the paper suggests leveraging the breadth of perspectives to enhance model capability in binary inferencing tasks.

• Roles and Responsibilities:
  • Lawyer and Prosecutor: These roles utilize low-level models to argue for and against a statement respectively. They analyze a problem from distinct viewpoints, constructing arguments based on few-shot examples.
  • Judge: A high-level model acts as the judge, synthesizing arguments from both sides to produce comprehensive judgments and feedback.

This iterative process is repeated to refine arguments and enhance the quality of inferencing through multiple rounds of judgment, thus improving the model's accuracy by synthesizing diverse perspectives.

Experimental Evaluation

The JoT methodology was rigorously tested on benchmark datasets such as BigBenchHard and Winogrande, demonstrating superior performance over traditional prompt engineering methods. Notably, the experimental results highlight JoT's substantial performance gains:

• Boolean Expressions Task: Achieving up to 96% accuracy and 0.97 F1 Score, JoT exceeded competitor methodologies, revealing its advanced logic processing capabilities.
• Real-World Application: In tasks like Fake News Detection, JoT's performance was noteworthy, commonly achieving higher accuracy and F1 scores than both zero-shot and few-shot methods, with benchmarks indicating superior precision and recall.

Implications and Future Directions

JoT's results suggest significant practical implications. In particular, the technique can be extended across various domains where binary reasoning tasks are pivotal, such as legal document analysis and information verification systems. Given its promising accuracy and reliability, JoT may be particularly impactful in sectors requiring advanced logical reasoning and multidimensional analysis.

However, real-world applicability presents challenges, such as increased computational cost and the necessity to address biases intrinsic to diverse data sets. Moreover, the integration of domain-specific knowledge remains a critical roadblock.

Future research should focus on refining the JoT framework, particularly in optimizing computational efficiency and integrating domain-specific knowledge without compromising generalizability. Expanding JoT's scope to accommodate broader real-world applications through customization of model components based on unique domain requirements would further establish its utility.

In conclusion, JoT represents a significant step forward in prompt engineering for LLMs, offering a structured approach that enhances binary logical reasoning. While the paper acknowledges certain limitations and challenges, the proposed technique has opened pathways for further innovation and application within the AI and NLP communities, underscoring the potential of role-based reasoning frameworks in complex language processing tasks.