Tornike Tsereteli

I am a Machine Learning (ML) Researcher and Engineer with a focus on Natural Language Processing (NLP). I am passionate about using ML to solve complex, real-world problems. My present focus is on improving the performance of Large Language Models (LLMs) in domain-specific and multilingual settings. My research interests include Scholarly Document Processing, Multilingual NLP, Information Retrieval (e.g., for Retrieval-Augmented Generation), and AI for Software Engineering.

latest posts

Jul 13, 2022	Survey Variable Identification
Oct 28, 2020	Rick and Morty story generation with GPT2 using Transformers and Streamlit in 57 lines of code

selected publications

(Under Review) Enriching Social Science Research via Survey Item Linking

Tornike Tsereteli, Daniel Ruffinelli, and Simone Paolo Ponzetto

2024

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VADIS – A Variable Detection, Interlinking and Summarization System

Yavuz Selim Kartal, Muhammad Ahsan Shahid, Sotaro Takeshita, and 4 more authors

In Advances in Information Retrieval, 2024

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The VADIS system addresses the demand of providing enhanced information access in the domain of the social sciences. This is achieved by allowing users to search and use survey variables in context of their underlying research data and scholarly publications which have been interlinked with each other.
Overview of the SV-Ident 2022 Shared Task on Survey Variable Identification in Social Science Publications

Tornike Tsereteli, Yavuz Selim Kartal, Simone Paolo Ponzetto, and 3 more authors

In Proceedings of the Third Workshop on Scholarly Document Processing, Oct 2022

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In this paper, we provide an overview of the SV-Ident shared task as part of the 3rd Workshop on Scholarly Document Processing (SDP) at COLING 2022. In the shared task, participants were provided with a sentence and a vocabulary of variables, and asked to identify which variables, if any, are mentioned in individual sentences from scholarly documents in full text. Two teams made a total of 9 submissions to the shared task leaderboard. While none of the teams improve on the baseline systems, we still draw insights from their submissions. Furthermore, we provide a detailed evaluation. Data and baselines for our shared task are freely available at \urlhttps://github.com/vadis-project/sv-ident.
Cross-lingual citations in English papers: a large-scale analysis of prevalence, usage, and impact

Tarek Saier, Michael Färber, and Tornike Tsereteli

Int. J. Digit. Libr., Jun 2022

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AbstractCitation information in scholarly data is an important source of insight into the reception of publications and the scholarly discourse. Outcomes of citation analyses and the applicability of citation-based machine learning approaches heavily depend on the completeness of such data. One particular shortcoming of scholarly data nowadays is that non-English publications are often not included in data sets, or that language metadata is not available. Because of this, citations between publications of differing languages (cross-lingual citations) have only been studied to a very limited degree. In this paper, we present an analysis of cross-lingual citations based on over one million English papers, spanning three scientific disciplines and a time span of three decades. Our investigation covers differences between cited languages and disciplines, trends over time, and the usage characteristics as well as impact of cross-lingual citations. Among our findings are an increasing rate of citations to publications written in Chinese, citations being primarily to local non-English languages, and consistency in citation intent between cross- and monolingual citations. To facilitate further research, we make our collected data and source code publicly available.