activity
20242026
collaborators

20 papers

cs.CL2026

A Systematic Evaluation of Cross-Lingual Consistency Enhancement Methods in Multilingual Language Models

Jirui Qi, Mingyang Wang, Hinrich Schütze +2

Multilingual language models often produce inconsistent answers to semantically equivalent questions across languages, motivating methods to improve cross-lingual consistency (CLC)…

cs.CL2026

Topics as Proxies for Sociodemographics: How Conversational Context Affects LLM Answers

Vera Neplenbroek, Gabriele Sarti, Arianna Bisazza +1

When large language models (LLMs) are used in high-stakes scenarios, such as legal, medical and financial advice, even a single conversation history is enough to drive differences…

cs.CL2026

Modeling Human-Like Color Naming Behavior in Context

Yuqing Zhang, Ecesu Ürker, Tessa Verhoef +2

Modeling the emergence of human-like lexicons in computational systems has advanced through the use of interacting neural agents, which simulate both learning and communicative pre…

cs.CL2026

Post-Training Language Models for Crosslingual Consistency

Tianyu Liu, Jirui Qi, Mrinmaya Sachan +3

Language models often respond inconsistently to translation-equivalent prompts across languages, undermining the reliability of multilingual systems. To quantify this, we give an i…

cs.CL2025

Challenging the Abilities of Large Language Models in Italian: a Community Initiative

Malvina Nissim, Danilo Croce, Viviana Patti +78

The rapid progress of Large Language Models (LLMs) has transformed natural language processing and broadened its impact across research and society. Yet, systematic evaluation of t…

cs.CL2025

Child-Directed Language Does Not Consistently Boost Syntax Learning in Language Models

Francesca Padovani, Jaap Jumelet, Yevgen Matusevych +1

Seminal work by Huebner et al. (2021) showed that language models (LMs) trained on English Child-Directed Language (CDL) can reach similar syntactic abilities as LMs trained on muc…