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Natural Language Processing for Requirements Formalization: How to Derive New Approaches?

Published in Concurrency, Specification and Programming: Revised Selected Papers from the 29th International Workshop on Concurrency, Specification and Programming (CS&P'21), Berlin, Germany, 2023

We present and discuss principal ideas and state-of-the-art methodologies from the field of NLP in order to guide the readers on how to derive new requirements formalization approaches according to their specific use case and needs. We demonstrate our approaches on two industrial use cases from the automotive and railway domains and show that the use of current pre-trained NLP models requires less effort to adapt to a specific use case.

Recommended citation: Sudhi, V., Kutty, L., Gröpler, R. (2023). Natural Language Processing for Requirements Formalization: How to Derive New Approaches?. In: Schlingloff, BH., Vogel, T., Skowron, A. (eds) Concurrency, Specification and Programming. Studies in Computational Intelligence, vol 1091. Springer, Cham. https://doi.org/10.1007/978-3-031-26651-5_1
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CarExpert: Leveraging Large Language Models for In-Car Conversational Question Answering

Published in Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: Industry Track, 2023

We propose CarExpert, an in-car retrieval-augmented conversational question-answering system leveraging LLMs for different tasks. Specifically, CarExpert employs LLMs to control the input, provide domain-specific documents to the extractive and generative answering components, and controls the output to ensure safe and domain-specific answers.

Recommended citation: Md Rashad Al Hasan Rony, Christian Suess, Sinchana Ramakanth Bhat, Viju Sudhi, Julia Schneider, Maximilian Vogel, Roman Teucher, Ken Friedl, and Soumya Sahoo. 2023. CarExpert: Leveraging Large Language Models for In-Car Conversational Question Answering. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: Industry Track, pages 586–604, Singapore. Association for Computational Linguistics.
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Illuminer: Instruction-tuned large language models as few-shot intent classifier and slot filler

Published in Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), 2024

Introducing ILLUMINER, an approach framing IC and SF as language generation tasks for Instruct-LLMs, with a more efficient SF-prompting method compared to prior work.

Recommended citation: Paramita Mirza, Viju Sudhi, Soumya Ranjan Sahoo, and Sinchana Ramakanth Bhat. 2024. ILLUMINER: Instruction-tuned Large Language Models as Few-shot Intent Classifier and Slot Filler. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 8639–8651, Torino, Italia. ELRA and ICCL.
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RAG-Ex: A Generic Framework for Explaining Retrieval Augmented Generation

Published in Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2024

Introduce RAG-Ex, a model- and language-agnostic explanation framework that presents approximate explanations to the users revealing why the LLMs possibly generated a piece of text as a response,

Recommended citation: Viju Sudhi, Sinchana Ramakanth Bhat, Max Rudat, and Roman Teucher. 2024. RAG-Ex: A Generic Framework for Explaining Retrieval Augmented Generation. In Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '24). Association for Computing Machinery, New York, NY, USA, 2776–2780. https://doi.org/10.1145/3626772.3657660
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teaching

Teaching experience 1

Undergraduate course, University 1, Department, 2014

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Teaching experience 2

Workshop, University 1, Department, 2015

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