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cs.CL2026
Distinguishing Artificial from Authentic: Evaluating LLMs for Detecting LLM-Generated Content
Juho Leinonen, Paul Denny
As large language models (LLMs) are increasingly used by students to generate natural language responses and program code, there is growing interest in whether LLMs themselves can…
cs.CL2026
RLearner-LLM: Balancing Logical Grounding and Fluency in Large Language Models via Hybrid Direct Preference Optimization
Qiming Bao, Juho Leinonen, Paul Denny +1
Direct Preference Optimization (DPO), the efficient alternative to PPO-based RLHF, falls short on knowledge-intensive generation: standard preference signals from human annotators…
cs.CL2026
When Looks Do Not Lie: Discourse Structure Guided In-Context Learning for Faithful Diagram Generation
Evanfiya Logacheva, Arto Hellas, Tsvetomila Mihaylova +3
GenAI is widespread in educational applications; however, it is known to generate content with intrinsic and extrinsic hallucination. We introduce a novel method for ICL diagram ge…