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Andres Algaba

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…

2023

LUCID: Exposing Algorithmic Bias through Inverse Design

AAAI 2023technical

AI systems can create, propagate, support, and automate bias in decision-making processes. To mitigate biased decisions, we both need to understand the origin of the bias and define what it means for an algorithm to make fair decisions. Most group fairness notions assess a model's equality of outcom…

Cited by 3SourcePDFScholar