collaborators

6 papers

cs.IR2026

Claim2Source at CheckThat! 2026: Improving Multilingual Scientific Claim-Source Retrieval with Verification-based Re-Ranking

Tobias Schreieder, Harsh Khandelwal, Yu-Ling Zhong +1

Multilingual scientific claim-source retrieval aims to identify the scientific publication supporting a claim shared on social media. This task is challenging because claims often…

cs.CL2026

Attribution, Citation, and Quotation: A Survey of Evidence-based Text Generation with Large Language Models

Tobias Schreieder, Tim Schopf, Michael Färber

The increasing adoption of large language models (LLMs) has raised serious concerns about their reliability and trustworthiness. As a result, a growing body of research focuses on…

cs.CL2026

Is this Idea Novel? An Automated Benchmark for Judgment of Research Ideas

Tim Schopf, Michael Färber

Judging the novelty of research ideas is crucial for advancing science, enabling the identification of unexplored directions, and ensuring contributions meaningfully extend existin…

cs.IR2025

SQuAI: Scientific Question-Answering with Multi-Agent Retrieval-Augmented Generation

Ines Besrour, Jingbo He, Tobias Schreieder +1

We present SQuAI (https://squai.scads.ai/), a scalable and trustworthy multi-agent retrieval-augmented generation (RAG) framework for scientific question answering (QA) with large…

cs.CL2025

DocIE@XLLM25: In-Context Learning for Information Extraction using Fully Synthetic Demonstrations

Nicholas Popovič, Ashish Kangen, Tim Schopf +1

Large, high-quality annotated corpora remain scarce in document-level entity and relation extraction in zero-shot or few-shot settings. In this paper, we present a fully automatic,…

cs.CL2025

Revisiting Projection-based Data Transfer for Cross-Lingual Named Entity Recognition in Low-Resource Languages

Andrei Politov, Oleh Shkalikov, René Jäkel +1

Cross-lingual Named Entity Recognition (NER) leverages knowledge transfer between languages to identify and classify named entities, making it particularly useful for low-resource…