arXiv Computation and Language
Sep 25

Script Choice in LLMs: Evidence for Late-Layer Commitment

The paper examines how large language models (LLMs) encode script knowledge across their layers using logistic regression probing and logit‑lens analysis. Findings show that both the input and instructed output scripts are represented in the earliest layers, but the model commits to the actual output script only in the final layers, with intermediate representations defaulting to Latin. This two‑stage process is consistent across methods and is more pronounced in larger models, suggesting a link between model depth and script commitment.

By David Kletz, Sandra Mitrovi\'c, Itay Sabato, Ljiljana Dolami\'c, Fabio Rinaldi
arXiv Computation and Language
Aug 28

Not Truly Multilingual: Script Consistency as a Missing Dimension in VLM Evaluation

The paper introduces PuMVR, a benchmark of 1,000 Punjabi image‑text pairs spanning three scripts—Gurmukhi, Shahmukhi, and Roman—to evaluate Vision‑Language Models (VLMs). Testing ten state‑of‑the‑art VLMs reveals a significant Script Gap: models perform well in one script but poorly in another, with accuracy differences up to 16%. The authors propose the Script Consistency Rate (SCR) as a new metric, noting it can be as low as 24.8% on their benchmark, and argue that current multilingual VLMs are not truly multi‑script.

By Prabhjot Singh, Bhushan Pawar, Madhu Reddiboina, Rajvee Sheth
Hugging Face Trending Papers
Jul 16

Large Language Models for Code Generation from Multilingual Prompts: A Curated Benchmark and a Study on Code Quality

Large Language Models (LLMs) perform differently on identical programming tasks when prompted in different natural languages, a phenomenon known as language bias. While this behavior has been widely studied for general text generation, its impact on code generation quality and programming conventions remains largely unexplored.

arXiv AI
Jul 17

Large Language Models for Code Generation from Multilingual Prompts: A Curated Benchmark and a Study on Code Quality

arXiv:2607. 14816v1 Announce Type: cross Abstract: Large Language Models (LLMs) perform differently on identical programming tasks when prompted in different natural languages, a phenomenon known as language bias.

By Saima Afrin, Alessandro Midolo, Camilo Escobar-Vel\'asquez, Mario Linares-V\'asquez, Weiyuan Ding, Bowen Xu, Massimiliano Di Penta, Antonio Mastropaolo