arXiv AI
Aug 25

Lexical Perturbations Disrupt LLM Reasoning: An Empirical Study of Attention Diversion

The study investigates how lexical perturbations—such as keyboard noise, character swaps, and filler insertion—affect large language models (LLMs) on reasoning benchmarks. Four open-weight instruction-tuned models and frontier models were evaluated, revealing that character-level perturbations significantly reduce accuracy, especially on multi-step reasoning tasks, while filler insertion has minimal impact. The authors attribute this asymmetry to Attention Diversion, where fragmented subword tokenization draws disproportionate attention in middle and final transformer layers; they demonstrate that both token content and attention allocation are coupled, making it difficult for inference-time repair strategies to fully recover performance.

By Jiaqian Zhu, Yang Zhang, Junhua Ding, Xiaowei Yu
arXiv Computer Vision
4d ago

What Can Low Resource Languages Learn From Each Other?

The paper examines OCR adaptation for low‑resource languages, noting that fine‑tuning often hits a performance ceiling in data‑scarce settings. It identifies that lower layers of language‑specific models learn redundant features while higher layers capture script nuances, leading to a structural inefficiency. To address this, the authors propose PSMC, a framework that pre‑trains a base model, specializes it per language, merges the experts via task arithmetic, and co‑trains a unified multilingual backbone, achieving about a 2% improvement in Word Recognition Rate across 10 Indian scripts without adding parameters.

By Achyuth P, Kahaan Shah, Chetan Arora