Soft Inductive Bias Approach via Explicit Reasoning Perspectives in Inappropriate Utterance Detection Using Large Language Models

TL;DR

This paper introduces a soft inductive bias method with explicit reasoning perspectives to improve inappropriate utterance detection using large Korean language models, achieving higher accuracy and more rational judgments.

Contribution

It proposes a novel approach that guides large language models with explicit reasoning perspectives, enhancing their ability to detect inappropriate utterances more accurately.

Findings

Kanana-1.5 model achieves 87.0046% accuracy

Method improves accuracy by approximately 3.89% over standard supervised learning

Explicit reasoning perspectives lead to more precise and consistent judgments

Abstract

Recent incidents in certain online games and communities, where anonymity is guaranteed, show that unchecked inappropriate remarks frequently escalate into verbal abuse and even criminal behavior, raising significant social concerns. Consequently, there is a growing need for research on techniques that can detect inappropriate utterances within conversational texts to help build a safer communication environment. Although large-scale language models trained on Korean corpora and chain-of-thought reasoning have recently gained attention, research applying these approaches to inappropriate utterance detection remains limited. In this study, we propose a soft inductive bias approach that explicitly defines reasoning perspectives to guide the inference process, thereby promoting rational decision-making and preventing errors that may arise during reasoning. We fine-tune a Korean large language model using the proposed method and conduct both quantitative performance comparisons and qualitative evaluations across different training strategies. Experimental results show that the Kanana-1.5 model achieves an average accuracy of 87.0046, improving by approximately 3.89 percent over standard supervised learning. These findings indicate that the proposed method goes beyond simple knowledge imitation by large language models and enables more precise and consistent judgments through constrained reasoning perspectives, demonstrating its effectiveness for inappropriate utterance detection.