인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2026.6
- 수록면
- 902 - 912 (11page)
- DOI
- 10.5302/J.ICROS.2026.25.0339
이용수
초록· 키워드
This paper presents a unified framework for controllable text generation in large language models (LLMs), integrating anchor-based latent space alignment with hidden-state adversarial learning. The framework aims to achieve fine-grained difficulty control in educational content generation, particularly in math problem construction. The proposed method leverages 1) a difficulty anchor module that provides directional semantic guidance in latent space and 2) a hidden-sequence discriminator that enforces distributional consistency with real examples, enabling end-to-end gradient-based optimization during training via continuous relaxation without reinforcement learning or attribute-specific classifiers. The model is fine-tuned via low-rank adaptation (LoRA) and trained to generate intermediate-difficulty math problems by interpolating between easy and hard inputs. Experiments conducted across three backbone LLMs—Qwen2.5-Math-7B, LLaMA-3.2-3B, and EXAONE-4.0-1.2B—demonstrate consistent performance gains. On average, the proposed method improves target difficulty accuracy from 37.3% to 49.0% and F1 score from 0.281 to 0.356, representing a substantial enhancement in controllability. Beyond attribute accuracy, the model also improves semantic quality (BERTScore F1 = 0.660), topic relevance (QRelScore = 0.3758), answerability (RQUGE = 1.5922), and fluency (Perplexity = 4.5605). Ablation studies further confirm that anchor loss and adversarial loss contribute complementary effects, yielding the strongest performance when jointly optimized. These results highlight the effectiveness and scalability of anchor-guided adversarial learning for controllable text generation. The proposed framework offers a practical direction for adaptive learning systems and can be extended to multi-attribute control, personalized difficulty adjustment, and continuous attribute modeling.
#controllable text generation
#latent space alignment
#adversarial learning
#large language models
#math problem
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목차
- Abstract
- I. INTRODUCTION
- II. RELATED WORK
- III. PROPOSED METHOD
- IV. EXPERIMENTS
- V. CONCLUSION
- REFERENCES
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