Research outputs
2025
Dissecting Bias in LLMs: A Mechanistic Interpretability Perspective
Unraveling the Influence of Training Data and Internal Structures in Large Language Models for Enhanced Explainability (Student Abstract)
Li, L., & Sen, P. (2025). Unraveling the Influence of Training Data and Internal Structures in Large Language Models for Enhanced Explainability (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 39(28), 29407-29409. doi:10.1609/aaai.v39i28.35268
Regularized Gradient Clipping Provably Trains Wide and Deep Neural Networks
Tucat, M., Mukherjee, A., Sen, P., Sun, M., & Rivasplata, O. (2025). Regularized Gradient Clipping Provably Trains Wide and Deep Neural Networks. Transactions on Machine Learning Research, 2025-June.
2024
A case study for automated attribute extraction from legal documents using large language models
Adhikary, S., Sen, P., Roy, D., & Ghosh, K. (2024). A case study for automated attribute extraction from legal documents using large language models. Artificial Intelligence and Law. doi:10.1007/s10506-024-09425-7
Knowledge Base-enhanced Multilingual Relation Extraction with Large Language Models
Chen, T., Sen, P., Wang, Z., Jiang, Z., & Su, J. (2024). Knowledge Base-enhanced Multilingual Relation Extraction with Large Language Models. In Ceur Workshop Proceedings Vol. 3818 (pp. 47-58).
Report on the 2nd Symposium on NLP for Social Good.
Sen, P., Saha, T., & Bollegala, D. (2024). Report on the 2nd Symposium on NLP for Social Good.. In P. Sen, T. Saha, & D. Bollegala (Eds.), NSG Vol. 3764. CEUR-WS.org. Retrieved from https://ceur-ws.org/Vol-3764
Simulated Task Oriented Dialogues for Developing Versatile Conversational Agents
Wang, X., Sen, P., Li, R., & Yilmaz, E. (2024). Simulated Task Oriented Dialogues for Developing Versatile Conversational Agents. In Lecture Notes in Computer Science (pp. 157-172). Springer Nature Switzerland. doi:10.1007/978-3-031-56027-9_10
2023
Explainable Information Retrieval
Anand, A., Sen, P., Saha, S., Verma, M., & Mitra, M. (2023). Explainable Information Retrieval. In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 3448-3451). ACM. doi:10.1145/3539618.3594249
Task2KB: A Public Task-Oriented Knowledge Base
Sen, P., Wang, X., Xu, R., & Yilmaz, E. (2023). Task2KB: A Public Task-Oriented Knowledge Base. In Proceedings of the AAAI Conference on Artificial Intelligence Vol. 37 (pp. 16482-16484). Association for the Advancement of Artificial Intelligence (AAAI). doi:10.1609/aaai.v37i13.27086
A Word Sense Distribution-based approach for Semantic Change Prediction
Tang, X., Zhou, Y., Aida, T., Sen, P., & Bollegala, D. (2023). A Word Sense Distribution-based approach for Semantic Change Prediction. In Findings of the Association for Computational Linguistics: EMNLP 2023 (pp. 3575-3590). Association for Computational Linguistics. doi:10.18653/v1/2023.findings-emnlp.231
Can Word Sense Distribution Detect Semantic Changes of Words?
Tang, X., Zhou, Y., Aida, T., Sen, P., & Bollegala, D. (2023). Can Word Sense Distribution Detect Semantic Changes of Words?. In Findings of the Association for Computational Linguistics Emnlp 2023 (pp. 3575-3590). doi:10.18653/v1/2023.findings-emnlp.231
Finding Important Arguments from a Legal Case
Konstantynowicz, D., Wojciechowski, F. G., & Sen, P. (2023). Finding Important Arguments from a Legal Case. In Ceur Workshop Proceedings Vol. 3614 (pp. 33-37).
2022
Measuring and Comparing the Consistency of IR Models for Query Pairs with Similar and Different Information Needs
Sen, P., Saha, S., Ganguly, D., Verma, M., & Roy, D. (2022). Measuring and Comparing the Consistency of IR Models for Query Pairs with Similar and Different Information Needs. In Proceedings of the 31st ACM International Conference on Information & Knowledge Management (pp. 4449-4453). ACM. doi:10.1145/3511808.3557637
Workshop on Proactive and Agent-Supported Information Retrieval (PASIR)
Jones, G. J. F., Sen, P., Ganguly, D., & Yilmaz, E. (2022). Workshop on Proactive and Agent-Supported Information Retrieval (PASIR). In Proceedings of the 31st ACM International Conference on Information & Knowledge Management (pp. 5167-5168). ACM. doi:10.1145/3511808.3557939
I Know What You Need: Investigating Document Retrieval Effectiveness with Partial Session Contexts
Sen, P., Ganguly, D., & Jones, G. J. F. (2022). I Know What You Need: Investigating Document Retrieval Effectiveness with Partial Session Contexts. ACM Transactions on Information Systems, 40(3), 1-30. doi:10.1145/3488667