Guido Rocchietti - Researcher

About Me

I am a PostDoc Researcher at the Data Integration and Data Preparation (D2IP) group within BIFOLD (Technische Universität Berlin), specializing in Information Integration and Data Cleansing. I hold a Ph.D. in AI for Society from the University of Pisa and ISTI-CNR, with a thesis on efficient methods for conversational search and summarization. My research interests include Large Language Models (LLMs), Conversational Search, Information Retrieval, Data Integration, and Text Summarization.

Research

My research focuses on Artificial Intelligence and Natural Language Processing (NLP). I was involved in the following projects:

EFRA (Food Risk Analytics)

ATRIUM (Digital Humanities)

FAIR (Future AI Research)

Tech Stack

Publications

PhD Thesis

Efficient and Effective Methods for Conversational Search and Summarization (2025). Link to Thesis

Peer-reviewed Conference Papers

Muntean, C. I., Nardini, F. M., Perego, R., Rocchietti, G., & Rulli, C. (2025). Efficient Conversational Search via Topical Locality in Dense Retrieval (short paper). In SIGIR '25. DOI:10.1145/3726302.3730186

Galimzhanova, E., Muntean, C. I., Nardini, F. M., Perego, R., & Rocchietti, G. (2023). Rewriting Conversational Utterances with Instructed Large Language Models. In 2023 IEEE/WIC International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT). DOI:10.1109/WI-IAT59888.2023.00014

Rocchietti, G., Frieder, O., Muntean, C. I., Nardini, F. M., & Perego, R. (2023). Commonsense Injection in Conversational Systems: An Adaptable Framework for Query Expansion. In 2023 IEEE/WIC International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT). DOI:10.1109/WI-IAT59888.2023.00013

Rocchietti, G., Achena, F., Marziano, G., Salaris, S., & Lenci, A. (2021). FANCY: A Diagnostic Data-Set for NLI Models. In Eighth Italian Conference on Computational Linguistics CliC-It 2021. DOI:10.4000/books.aaccademia.10804

Rocchietti, G., Pugliese, C., Rangel, G. S., & Carvalho, J. T. (2024). From Geolocated Images to Urban Region Identification and Description: a Large Language Model Approach (short paper). In Proceedings of the 32nd ACM International Conference on Advances in Geographic Information Systems, SIGSPATIAL '24. DOI:10.1145/3678717.3691317

Rocchietti, G., Rulli, C., Randl, K., Muntean, C. I., Nardini, F. M., Perego, R., Trani, S., Karvounis, M., & Janostik, J. (2024). LongDoc Summarization using Instruction-tuned Large Language Models for Food Safety Regulations. In IIR2024: 14th Italian Information Retrieval Workshop.

Peer-reviewed Journal Articles

Rocchietti, G., Rulli, C., Nardini, F. M., Muntean, C. I., Perego, R., & Frieder, O. (2025). ChatGPT Versus Modest Large Language Models: An Extensive Study on Benefits and Drawbacks for Conversational Search. IEEE Access, 13. DOI:10.1109/ACCESS.2025.3529741

Non-peer-reviewed Articles

Rocchietti, G., Muntean, C. I., & Nardini, F. M. (2024). Enhancing Conversational Search with Large Language Models. ERCIM News, 136.

Featured Research Software

efficient_retrieval: Algorithms supporting efficient conversational retrieval.

llms4queryrewriting: Extensive study on the benefits and drawbacks of using LLMs for query rewriting.

UrbanRegionDescription: NLP methodology for the automatic identification of urban regions from geolocated images.

conv-common-sense: Adaptable framework for query expansion using commonsense knowledge.

FANCY: Diagnostic dataset for evaluating Natural Language Inference (NLI) models.