Langhui Technology deeply engages in general corpus AI data services, building 20 high-quality datasets across four major domains—multilingual corpora, cross-cultural data, general pretraining corpora, and data cleaning systems—with 100TB+ of corpora, 24 languages, and 500B+ tokens, empowering large-model pretraining with high-quality, multilingual, and cross-cultural corpora to accelerate Artificial General Intelligence.
From GPT-4 multilingual to cross-lingual transfer, from value alignment to general pretraining, AI is accelerating toward Artificial General Intelligence (AGI).
OpenAI releases the GPT-4 multilingual version, covering 95+ languages. It achieves SOTA on cross-lingual understanding, machine translation, and low-resource language tasks, with multilingual reasoning approaching native-level proficiency, driving global AI inclusion.
Meta releases an upgraded XLM-RoBERTa, pretrained on 100+ languages and enabling zero-shot cross-lingual transfer. It performs outstandingly on low-resource language tasks, and its open-source release advances global multilingual NLP research.
Anthropic proposes the Constitutional AI 2.0 framework, strengthening cultural and value alignment with multi-cultural value evaluation benchmarks, mitigating cultural bias, and building responsible general artificial intelligence.
Google releases the XTREME-Plus multilingual evaluation benchmark, covering 100+ languages across four task types—classification, QA, retrieval, and reasoning—comprehensively assessing large-model cross-lingual capability, becoming the standard benchmark for multilingual models.
The ACL 2025 Best Paper focuses on low-resource language NLP, proposing methods based on transfer learning and few-shot learning for low-resource language models, achieving significant breakthroughs on Swahili, Bengali, and other low-resource languages.
DeepSeek 2026 introduces a new-generation general pretraining solution, delivering 3× large-scale pretraining efficiency gains and 60% lower training costs. It reaches GPT-4 level on multilingual and multi-task benchmarks, and its open-source release drives industry-wide accessibility.
Building on a trusted data foundation to deliver end-to-end data infrastructure for large-model pretraining.
Covering 24 languages, including Chinese, English, Japanese, Korean, French, German, Spanish, Russian, and Arabic—reaching 90% of the global population and forming the foundation for multilingual LLM training.
Cultural alignment, value alignment, and regional adaptation data that mitigate cultural bias in LLMs and build responsible general artificial intelligence.
Five major corpus types—web, books, news, encyclopedias, and code—totaling 100TB+ of high-quality data to support general LLM foundation training.
End-to-end cleaning pipeline—deduplication, filtering, quality assessment, and safety review—yielding 500B+ cleaned tokens to ensure pretraining data quality.
Four core metrics fully meeting standards, building a trusted general corpus infrastructure.
Covers 24 mainstream languages used by 90% of the global population.
Multi-dimensional quality assessment—98% of data meets the high-quality standard.
Cross-cultural expert review—95% of data passes the cultural alignment verification.
Industrialized cleaning pipeline—92% of raw corpora retained as high-quality data.
20 high-quality datasets covering the full range of LLM pretraining scenarios.
Chinese-English bilingual aligned corpora covering news, encyclopedias, and literature, supporting machine translation and cross-lingual model training.
24-language parallel corpus covering the 6 official UN languages and mainstream low-resource languages, supporting multilingual LLM training.
Low-resource language corpora including Swahili, Bengali, and Thai, supporting low-resource NLP and cross-lingual transfer research.
Eastern-Western cultural value aligned corpora with cultural context, customs, taboos, and social etiquette annotations to mitigate LLM cultural bias.
Value alignment dataset based on Constitutional AI, with human feedback, preference comparison, and safety constraint annotations.
Corpora across different regional, ethnic, and religious backgrounds, with regional features, cultural taboos, and localization annotations to support LLM regional adaptation.
High-quality web corpora cleaned from Common Crawl, covering multiple domains and languages—the core source of pretraining data.
Multilingual book corpus covering literature, science, history, and philosophy—long-form, high-quality pretraining data.
News corpora from major global media outlets, covering 24 languages and multiple domains with strong timeliness—ideal for pretraining and fine-tuning.
Multilingual encyclopedia corpora such as Wikipedia, with rich structured knowledge covering 300+ subject areas—a high-quality knowledge source.
Multilingual code corpora from GitHub, covering 50+ programming languages including Python, Java, and C++, supporting code LLM training.
Multilingual instruction-following dataset with task instructions, responses, and quality scores, supporting instruction fine-tuning (SFT).
RLHF human feedback data with multilingual response rankings, preference comparisons, and safety constraints, supporting RLHF alignment training.
Multilingual response preference comparison dataset with preference labels and rationale, supporting DPO and other preference optimization training.
High-quality corpus deduplicated via MinHash and SimHash, removing duplicate and near-duplicate text to improve training efficiency.
Corpus filtered to remove toxic, low-quality, and harmful content, with content safety annotations to ensure pretraining data safety and compliance.
Corpus data with quality scores, multi-dimensionally assessing text quality to support high-quality pretraining data selection.
Trusted datasets, custom data services, and certified expert networks—three business lines working end-to-end to empower general LLM training.
20 ready-to-use standardized general corpus datasets covering five scenarios—multilingual, cross-cultural, pretraining, alignment, and cleaning. Standardized JSON/text formats, plug-and-play, supporting fast LLM training.
Industrial-grade cleaning, alignment annotation, and custom services—on-demand corpus cleaning, cross-cultural alignment, and instruction annotation solutions. Self-developed AI-assisted platform with 10× annotation efficiency.
Multilingual expert and cultural consultant review network, partnering with global linguists and cultural scholars to ensure corpus professionalism and cross-cultural accuracy.
Six typical AI application scenarios powered by Langhui's general corpus datasets.
Backed by 100TB+ multilingual high-quality corpora, supporting general LLM foundation training and accelerating the deployment of Artificial General Intelligence (AGI).
Multilingual parallel corpora supporting cross-lingual understanding, machine translation, and cross-lingual retrieval, covering 24 languages with seamless interoperability.
Cross-cultural data supporting LLM cultural alignment and regional adaptation, mitigating cultural bias and promoting global cultural exchange.
Multilingual high-quality corpora supporting multilingual content generation, covering news, literature, marketing, and technical documentation.
Multilingual customer-service dialogue corpora supporting multilingual intelligent customer service systems, delivering 24/7 global customer support.
Multilingual corpora supporting multilingual search and personalized recommendation systems, enhancing global user search and recommendation experiences.
Langhui Technology's general corpus datasets provide high-quality, multilingual, and cross-cultural corpora for LLM pretraining, accelerating the deployment of Artificial General Intelligence (AGI).