Layered Chain-of-Thought Prompting for Multi-Agent LLM Systems: A Comprehensive Approach to Explainable Large Language Models

Large Language Models (LLMs) leverage chain-of-thought (CoT) prompting to provide step-by-step rationales, improving performance on complex tasks. Despite its benefits, vanilla CoT often fails to fully verify intermediate inferences and can produce misleading explanations. In this work, we propose Layered Chain-of-Thought (Layered-CoT) Prompting, a novel framework that systematically segments the reasoning process into multiple layers, each subjected to external checks and optional user feedback. We expand on the key concepts, present three scenarios -- medical triage, financial risk assessment, and agile engineering -- and demonstrate how Layered-CoT surpasses vanilla CoT in terms of transparency, correctness, and user engagement. By integrating references from recent arXiv papers on interactive explainability, multi-agent frameworks, and agent-based collaboration, we illustrate how Layered-CoT paves the way for more reliable and grounded explanations in high-stakes domains.

Evaluating Large Language Models Using Contrast Sets: An Experimental Approach

In the domain of Natural Language Inference (NLI), especially in tasks involving the classification of multiple input texts, the Cross-Entropy Loss metric is widely employed as a standard for error measurement. However, this metric falls short in effectively evaluating a model's capacity to understand language entailments. In this study, we introduce an innovative technique for generating a contrast set for the Stanford Natural Language Inference (SNLI) dataset. Our strategy involves the automated substitution of verbs, adverbs, and adjectives with their synonyms to preserve the original meaning of sentences. This method aims to assess whether a model's performance is based on genuine language comprehension or simply on pattern recognition. We conducted our analysis using the ELECTRA-small model. The model achieved an accuracy of 89.9% on the conventional SNLI dataset but showed a reduced accuracy of 72.5% on our contrast set, indicating a substantial 17% decline. This outcome led us to conduct a detailed examination of the model's learning behaviors. Following this, we improved the model's resilience by fine-tuning it with a contrast-enhanced training dataset specifically designed for SNLI, which increased its accuracy to 85.5% on the contrast sets. Our findings highlight the importance of incorporating diverse linguistic expressions into datasets for NLI tasks. We hope that our research will encourage the creation of more inclusive datasets, thereby contributing to the development of NLI models that are both more sophisticated and effective.