Published in MAGMaR @ ACL 2025, 2025
Introduces a multimodal retrieval-augmented generation framework for spoken question answering without full transcription reliance.
Recommended citation: Rackauckas, Z., & Hirschberg, J. (2025). "VoxRAG: A Step Toward Transcription-Free RAG Systems in Spoken Question Answering." MAGMaR @ ACL 2025.
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Published in HCI+NLP @ EMNLP 2025, 2025
A dialogue system that integrates personal memory videos into emotionally grounded LLM-based question generation.
Recommended citation: Rackauckas, Z., Minematsu, N., & Hirschberg, J. (2025). "Re:Member: Emotional Question Generation from Personal Memories." HCI+NLP @ EMNLP 2025.
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Published in IEEE UEMCON 2025, 2025
Evaluates expressive Japanese TTS systems in character-based conversational settings.
Recommended citation: Rackauckas, Z., & Hirschberg, J. (2025). "Comparative Evaluation of Expressive Japanese Character Text-to-Speech with VITS and Style-BERT-VITS2." IEEE UEMCON 2025.
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Published in LLM4Eval @ SIGIR 2024, 2024
Proposes an automated Elo-based evaluation methodology for comparing retrieval-augmented generation systems.
Recommended citation: Rackauckas, Z., Camara, A., & Zavrel, J. (2024). "Evaluating RAG-Fusion with RAGElo: An Automated Elo-Based Framework." LLM4Eval @ SIGIR 2024.
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Published in JALTCALL Trends, 2025
Empirical analysis of learner engagement patterns in an LLM-driven character-based language learning application.
Recommended citation: Rackauckas, Z. (2025). "Learner Interactions with Jouzu, the Mobile Application with Conversational Characters." JALTCALL Trends.
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Published in International Journal on Natural Language Computing, 2024
Introduces RAG-Fusion, an improved retrieval-augmented generation approach for enhancing ranking robustness and generation reliability.
Recommended citation: Rackauckas, Z. (2024). "RAG-Fusion: A New Take on Traditional RAG." International Journal on Natural Language Computing, 13(1), 11.
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Published in International Conference on Human-Agent Interaction (HAI 2025), 2025
Extended abstract describing multimodal AI agents integrated into engineering lab instruction.
Recommended citation: Shigi, M., Rackauckas, Z., Akiyama, Y., & Minematsu, N. (2025). "LangInLab: Augmenting Engineering Lab Instruction with Vision- and Voice-Enabled AI Agents for Language Learning." HAI 2025.
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Published in arXiv, 2025
Preprint evaluating learning engagement and interaction outcomes in stylized LLM-driven dialogue agents.
Recommended citation: Rackauckas, Z., & Hirschberg, J. (2025). "Learning Japanese with Jouzu: Interaction Outcomes with Stylized Dialogue Fictional Agents." arXiv.
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