One ruler to measure them all: Benchmarking multilingual long-context language models
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ONERULER is a multilingual benchmark that adapts the English-only RULER to evaluate long-context language models across 26 languages.
A comprehensive index of artificial intelligence and machine-learning research with AI-generated summaries, citation metrics, and direct links to papers and code.
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ONERULER is a multilingual benchmark that adapts the English-only RULER to evaluate long-context language models across 26 languages.
P. Colombo, T. Pires, Malik Boudiaf, et al.
SaulLM-7B is a 7-billion-parameter LLM tailored for the legal domain, trained on over 30 billion tokens of English legal text and instruction-tuned to achieve state-of-the-art legal comprehension and generation.
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A 137M parameter open-source English text embedding model with 8192 context length outperforming OpenAI on short and long-context tasks.
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A bilingual Hindi-English language model built by continuously pre-training Nemotron-Mini 4B on 400B real and synthetic tokens.
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Nvidia's Nemotron-4 15B is a multilingual language model trained on 8 trillion tokens, achieving strong performance across English, code, and multilingual tasks.
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Multitask prompted fine-tuning on English tasks enables zero-shot cross-lingual generalization in large multilingual models.
Donghwa Lee, Hong-hyeon Kim, Seok-Hyun Sung
This paper develops an AI English learning support system based on a Learner-Generated Context framework to facilitate autonomous learning for Korean secondary-school students.
Sumie Tsz Sum Chan, Noble Po Kan Lo, Alan Man Him Wong
This paper shows that LLM-based feedback significantly improves university students' essay writing proficiency, engagement, and motivation, with mixed emotional experiences.