INSTRUCTION-GUIDED ALIGNMENT OF CHATBOTS BASED ON LARGE LANGUAGE MODELS FOR COMPLIANCE-CONSTRAINED FINANCIAL SCENARIOS - PISKUN, Oleksandr (Cherkasy University Bulletin: Applied Mathematics. Informatics, 2025)
Introduction. This paper investigates whether chatbots based on large language models (LLMs) can be safely used in regulated financial scenarios at the pre-sales stage of client interaction without model fine-tuning, provided that only instruction-based constraints are applied. Currency risk management is considered as a case study. Within the study, two chatbot configurations are designed and analyzed: a baseline (unconstrained) version and an instruction-constrained version oriented toward compliance requirements. For evaluation, a compact framework is proposed, covering three key dimensions: compliance violations, informativeness, and prescriptiveness. Based on a selected set of realistic user queries, it is demonstrated that instruction-based alignment can significantly reduce the model’s recommendation behavior while preserving a substantial portion of its explanatory value. Purpose. The aim of this study is to analyze the feasibility of safely using LLM-based chatbots in compliance-constrained financial scenarios, to develop an instruction-constrained chatbot configuration for pre-sales interaction in the domain of currency risk management, and to evaluate its effectiveness. Results. Design principles for deploying LLM-based chatbots in regulated financial environments are formulated, including role constraints, functional limitations, controlled response generation, and structured escalation mechanisms. These principles operationalize the practical application of instruction-based alignment without modifying model parameters. A compact, domain-specific evaluation framework for compliance-oriented conversational systems is proposed, combining binary safety indicators with graded measures of informativeness and prescriptiveness, supplemented by human evaluation procedures and inter-annotator agreement analysis. An empirical evaluation of instruction-based alignment under compliance constraints is conducted in the context of pre-sales financial interaction. In contrast to prior approaches that focus on alignment at the model level (e.g., RLHF or fine-tuning), this study isolates control at the usage stage, implemented through prompts and policy constraints. The results indicate that instruction-based alignment can effectively reduce compliance risks in the use of LLM-based chatbots in regulated financial scenarios. In particular, the application of clearly defined role constraints, prohibitions on recommendations, and structured response generation mechanisms substantially reduces recommendation behavior without significant loss of informativeness. Conclusion. The study demonstrates that instruction-based alignment is a practical approach to reducing compliance risks when deploying LLM-based chatbots in financial pre-sales scenarios. The proposed evaluation framework and empirical results show that a significant reduction in recommendation behavior can be achieved without substantial loss of explanatory capability.At the same time, instruction-based control should be considered as one component of a broader risk management system that includes monitoring, auditing, and human oversight. Future research should focus on expanding the empirical base, improving evaluation methodologies, and integrating instruction-based alignment with other approaches to ensure the safe use of artificial intelligence.