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# Yulia Tsvetkov · page 2

33 papers in our library · 2014–2026 · page 2 of 2

[The profile, affiliations and co-authors →](https://md.research.elamur.ai/free/en/authors/yulia-tsvetkov/)

## Papers

The most cited first (OpenAlex and our library).

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26. **[Native Language Cognate Effects on Second Language Lexical Choice](https://md.research.elamur.ai/free/en/papers/rabinovich-2018-native-language-cognate-effects-second/)** *(2018 · 52 citations)* Psycholinguistic research suggests that bilinguals favor second-language words that resemble equivalents in their mother tongue (cognate facilitation).
27. **[Resolving Knowledge Conflicts in Large Language Models](https://md.research.elamur.ai/free/en/papers/wang-2023-resolving-knowledge-conflicts-large-language/)** *(2023 · 48 citations)* When LLMs receive retrieved or prompted text, that text often contradicts what they absorbed during pretraining.
28. **[CulturalBench: A Robust, Diverse and Challenging Benchmark for Measuring LMs' Cultural Knowledge Through Human-AI Red-Teaming](https://md.research.elamur.ai/free/en/papers/chiu-2025-culturalbench-robust-diverse-challenging-benchmark/)** *(2025 · 38 citations)* Measuring how well language models understand everyday cultural practice worldwide is difficult. Static hand-written test sets soon become too easy. Benchmarks built from web pages, value surveys or synthetic generation may overlap with training data or carry over model biases.
29. **[Knowledge Card: Filling LLMs' Knowledge Gaps with Plug-in Specialized Language Models](https://md.research.elamur.ai/free/en/papers/feng-2023-knowledge-card-filling-llms-knowledge/)** *(2023 · 35 citations)* Large language models are frozen after training and costly to update. As a result, they often produce outdated, hallucinated, or missing facts, especially for long-tail and fast-changing topics. Knowledge Card addresses this by attaching small, domain-specific language models ("knowledge cards") to a black-box LLM.…
30. **[Cross-Cultural Similarity Features for Cross-Lingual Transfer Learning of Pragmatically Motivated Tasks](https://md.research.elamur.ai/free/en/papers/sun-2021-cross-cultural-similarity-features-cross/)** *(2021 · 28 citations)* Choosing a source language for cross-lingual transfer is usually guided by typological or genealogical closeness.
31. **[Biased LLMs can Influence Political Decision-Making](https://md.research.elamur.ai/free/en/papers/fisher-2025-biased-llms-can-influence-political/)** *(2025 · 20 citations)* The paper asks whether partisan bias in chat-based LLMs can change people's political views and choices during short, free-form conversations.
32. **[MAGNET: Improving the Multilingual Fairness of Language Models with Adaptive Gradient-Based Tokenization](https://md.research.elamur.ai/free/en/papers/ahia-2024-magnet-improving-multilingual-fairness-language/)** *(2024 · 19 citations)* Byte-level and subword language models usually split non-Latin-script text into many more pieces than English.
33. **[Among Us: Measuring and Mitigating Malicious Contributions in Model Collaboration Systems](https://md.research.elamur.ai/free/en/papers/yang-2026-among-us-measuring-mitigating-malicious/)** *(2026 · 1 citation)* Multi-LLM systems increasingly combine models built by different parties, which raises the question of how a compromised participant affects the whole.

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