---
title: "Who cites Biasless Language Models Learn — 3 citing papers"
description: "3 papers of the Elamur Research library cite «Biasless Language Models Learn Unnaturally: How LLMs Fail to Distinguish the Possible from…"
canonical: "https://research.elamur.ai/free/en/papers/ziv-2026-biasless-language-models-learn-unnaturally/cited-by/"
---
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[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Biasless Language Models Learn Unnaturally: How…](https://md.research.elamur.ai/free/en/papers/ziv-2026-biasless-language-models-learn-unnaturally/) / cited by

# Who cites «Biasless Language Models Learn Unnaturally: How LLMs Fail to Distinguish the Possible from the Impossible»

3 papers of our library cite it (of the 149,439 whose bibliographies we read) · 2 with a page here, the most cited first · [← back to «Biasless Language Models Learn Unnaturally: How LLMs Fail to Distinguish the Possible from the Impossible»](https://md.research.elamur.ai/free/en/papers/ziv-2026-biasless-language-models-learn-unnaturally/)

1. **[Function Words as Statistical Cues for Language Learning](https://md.research.elamur.ai/free/en/papers/yang-2026-function-words-as-statistical-cues/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xiulin Yang, Heidi R. Getz, Ethan Gotlieb Wilcox*2026 · 1 citation*
2. **[Vocabulary Shapes Cross-Lingual Variation of Word-Order Learnability in Language Models](https://md.research.elamur.ai/free/en/papers/martins-2026-vocabulary-shapes-cross-lingual-variation/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jonas Mayer Martins, Jaap Jumelet, Viola Priesemann, et al.*2026*

1 more paper of our library cites it; it has no page here yet.

- [← back to](https://md.research.elamur.ai/free/en/papers/ziv-2026-biasless-language-models-learn-unnaturally/) — Biasless Language Models Learn Unnaturally: How LLMs Fail to Distinguish the Possible from the Impossible
