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20232026
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cs.CL2026

Multilingual in Name Only? Cultural and Linguistic Weaknesses of LLMs in Urdu

Farah Adeeba, Abdul Rafae Khan, Rajesh Bhatt +1

Multilingual large language models (LLMs) are increasingly used for open-ended text generation, yet their behaviour in low-resource languages remains poorly understood. In this wor…

cs.CL2026

LLMs Underperform Graph-Based Parsers on Supervised Relation Extraction for Complex Graphs

Paolo Gajo, Domenic Rosati, Hassan Sajjad +1

Relation extraction represents a fundamental component in the process of creating knowledge graphs, among other applications. Large language models (LLMs) have been adopted as a pr…

cs.CL2026

LangFIR: Discovering Sparse Language-Specific Features from Monolingual Data for Language Steering

Sing Hieng Wong, Hassan Sajjad, A. B. Siddique

Large language models (LLMs) show strong multilingual capabilities, yet reliably controlling the language of their outputs remains difficult. Representation-level steering addresse…

cs.CL2025

Understanding Syntactic Generalization in Structure-inducing Language Models

David Arps, Hassan Sajjad, Laura Kallmeyer

Structure-inducing Language Models (SiLM) are trained on a self-supervised language modeling task, and induce a hierarchical sentence representation as a byproduct when processing…

cs.CL2025

UrBLiMP: A Benchmark for Evaluating the Linguistic Competence of Large Language Models in Urdu

Farah Adeeba, Brian Dillon, Hassan Sajjad +1

Multilingual Large Language Models (LLMs) have shown remarkable performance across various languages; however, they often include significantly less data for low-resource languages…

cs.CL2023

Multilingual Nonce Dependency Treebanks: Understanding how Language Models represent and process syntactic structure

David Arps, Laura Kallmeyer, Younes Samih +1

We introduce SPUD (Semantically Perturbed Universal Dependencies), a framework for creating nonce treebanks for the multilingual Universal Dependencies (UD) corpora. SPUD data sati…