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Vincent Holst

3 accepted papers

2026

Structurally Human, Semantically Biased: Detecting LLM-Generated References with Embeddings and GNNs

ICLR 2026poster

Large language models are increasingly used to curate bibliographies, raising the question: are their reference lists distinguishable from human ones? We build paired citation graphs, ground truth and GPT-4o-generated (from parametric knowledge), for 10,000 focal papers ($\approx$ 275k references) f…

Cited by 0SourceScholar
2025

Large Language Models Reflect Human Citation Patterns with a Heightened Citation Bias

NAACL 2025findings

Citation practices are crucial in shaping the structure of scientific knowledge, yet they are often influenced by contemporary norms and biases. The emergence of Large Language Models (LLMs) introduces a new dynamic to these practices. Interestingly, the characteristics and potential biases of refer…

2025

The Effectiveness of Curvature-Based Rewiring and the Role of Hyperparameters in GNNs Revisited

ICLR 2025poster

Message passing is the dominant paradigm in Graph Neural Networks (GNNs). The efficiency of message passing, however, can be limited by the topology of the graph. This happens when information is lost during propagation due to being oversquashed when travelling through bottlenecks. To remedy this, r…