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cs.CL2025
Explaining word embeddings with perfect fidelity: Case study in research impact prediction
Lucie Dvorackova, Marcin P. Joachimiak, Michal Cerny +3
The best-performing approaches for scholarly document quality prediction are based on embedding models. In addition to their performance when used in classifiers, embedding models…
cs.CL2025
Meaningless is better: hashing bias-inducing words in LLM prompts improves performance in logical reasoning and statistical learning
Milena Chadimová, Eduard Jurášek, Tomáš Kliegr
This paper introduces a novel method, referred to as "hashing", which involves masking potentially bias-inducing words in large language models (LLMs) with hash-like meaningless id…