Papers with language inference

3 papers
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (N19-1)

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Challenge: Existing language representation models pre-train deep bidirectional representations from unlabeled text without significant task-specific architecture modifications.
Approach: They propose a language representation model that pre-trains bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers.
Outcome: The proposed model achieves state-of-the-art results on eleven natural language processing tasks, pushing the GLUE score to 80.5 (7.7 point absolute improvement), MultiNLI accuracy to 86.7% (4.6% absolute improvement)
Evaluating Robustness of Large Language Models Against Multilingual Typographical Errors (2026.acl-long)

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Challenge: Large language models (LLMs) are increasingly deployed in multilingual, real-world applications where user inputs introduce typographical errors.
Approach: They propose a multilingual typo generation algorithm that simulates human-like errors based on language-specific keyboard layouts and typing behavior.
Outcome: The proposed model can generate the correct answer ("500") under typos in English, German, and Russian.
LORAXBENCH: A Multitask, Multilingual Benchmark Suite for 20 Indonesian Languages (2025.emnlp-main)

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Challenge: LORAXBENCH is a benchmark for low-resource languages of Indonesia . it covers reading comprehension, open domain QA, language inference, causal reasoning, translation, and cultural question answering across 20 languages.
Approach: They propose a benchmark that focuses on low-resource languages of Indonesia and covers 6 diverse tasks: reading comprehension, open-domain QA, language inference, causal reasoning, translation, and cultural question answering.
Outcome: The proposed benchmark covers reading comprehension, open-domain QA, language inference, causal reasoning, translation, and cultural question answering across 20 Indonesian languages.

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