Evaluating the Robustness of Japanese LLMs to IME-Related and Typographical Errors

arXiv:2610.01241 · cs.CL · Submitted 2026-10-01 · Read on arXiv

cs.CL

Submitted: 2026-10-01

Updated: 2026-10-01

Terminology

• Robustness — 39× in this paper · explained in 5 episodes
• Benchmark — 26× in this paper · explained in 2 episodes
• Accuracy — 20× in this paper · explained in 1 episode
• Activation Function — 6× in this paper · explained in 1 episode
• Large Language Models (LLMs) — 2× in this paper · explained in 6 episodes
• Tokenization — 2× in this paper · explained in 2 episodes
• Adaptation — 1× in this paper · explained in 1 episode
• Adversarial Attacks — 1× in this paper · explained in 1 episode
• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Generative AI — 1× in this paper · explained in 2 episodes
• Input Method Editor — 1× in this paper
• Instruction Tuning — 1× in this paper · explained in 2 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Resource Constraints — 1× in this paper · explained in 3 episodes
• Transformer — 1× in this paper · explained in 2 episodes
• Transformer Architecture — 1× in this paper · explained in 2 episodes

Sources

• GitHub Typo Corpus: A Large-Scale Multilingual Dataset of Misspellings and Grammatical Errors
• Noisy Text Data: Achilles' Heel of BERT
• GPT-4 Technical Report
• Misspellings in Natural Language Processing: A survey
• Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models
• LLaMA: Open and Efficient Foundation Language Models
• Llama 2: Open Foundation and Fine-Tuned Chat Models
• PromptRobust: Towards Evaluating the Robustness of Large Language Models on Adversarial Prompts

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