4 citations · 5 across the 6 of their papers we have counts for
6 papers
Think-Probe-Respond: Improving Large Language Models as Judges of Research Idea Novelty
Tim Schopf, Tobias Schreieder, Akiko Aizawa
Automated novelty judgment can accelerate scientific discovery by enabling efficient evaluation, refinement, and comparison of research ideas. While large language models are incre…
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…
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…
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…
RAGentA: Multi-Agent Retrieval-Augmented Generation for Attributed Question Answering
Ines Besrour, Jingbo He, Tobias Schreieder +1
We present RAGentA, a multi-agent retrieval-augmented generation (RAG) framework for attributed question answering (QA) with large language models (LLMs). With the goal of trustwor…
Slice it up: Unmasking User Identities in Smartwatch Health Data
Lucas Lange, Tobias Schreieder, Victor Christen +1
Wearables are widely used for health data collection due to their availability and advanced sensors, enabling smart health applications like stress detection. However, the sensitiv…