REVIEW 9 cited by
A Survey on Large Language Models with Multilingualism: Recent Advances and New Frontiers
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
The rapid development of Large Language Models (LLMs) demonstrates remarkable multilingual capabilities in natural language processing, attracting global attention in both academia and industry. To mitigate potential discrimination and enhance the overall usability and accessibility for diverse language user groups, it is important for the development of language-fair technology. Despite the breakthroughs of LLMs, the investigation into the multilingual scenario remains insufficient, where a comprehensive survey to summarize recent approaches, developments, limitations, and potential solutions is desirable. To this end, we provide a survey with multiple perspectives on the utilization of LLMs in the multilingual scenario. We first rethink the transitions between previous and current research on pre-trained language models. Then we introduce several perspectives on the multilingualism of LLMs, including training and inference methods, information retrieval, model security, multi-domain with language culture, and usage of datasets. We also discuss the major challenges that arise in these aspects, along with possible solutions. Besides, we highlight future research directions that aim at further enhancing LLMs with multilingualism. The survey aims to help the research community address multilingual problems and provide a comprehensive understanding of the core concepts, key techniques, and latest developments in multilingual natural language processing based on LLMs.
Forward citations
Cited by 9 Pith papers
-
Cross-Lingual Transfer of Cultural Knowledge: An Asymmetric Phenomenon
Cross-lingual transfer of cultural knowledge is bidirectional for high-resource languages and asymmetric for low-resource ones, with corpus frequency correlating with transfer success.
-
Dr.Copilot: A Multi-Agent Prompt Optimized Assistant for Improving Patient-Doctor Communication in Romanian
A multi-agent LLM assistant with DSPy-optimized prompts improved Romanian doctors' written communication quality and patient satisfaction in a live telemedicine deployment, but the evaluation is confounded by self-sel...
-
Under the Shadow of Babel: How Language Shapes Reasoning in LLMs
LLMs distribute attention differently for Chinese and English causal sentences, lose more accuracy on reversed Chinese order, yet converge to similar representations when they reason correctly.
-
The State of Multilingual LLM Safety Research: From Measuring the Language Gap to Mitigating It
LLM safety research at ACL venues from 2020 to 2024 is predominantly English-only, and the language gap is growing over time.
-
Fast and Cost-effective Speculative Edge-Cloud Decoding with Early Exits
Edge-cloud speculative decoding runs faster when early exits in the server model let the client pre-draft the next candidate tokens before final verification is complete.
-
When Models Meet Users: An Empirical Study of Perceptions of General LLMs and Multimodal LLMs on Hugging Face
Hugging Face discussions show that access barriers, output quality, and setup complexity are the main user concerns for both general and multimodal LLMs.
-
Less, but Better: Efficient Multilingual Expansion for LLMs via Layer-wise Mixture-of-Experts
A layer-wise expert allocation algorithm based on hidden-state similarity, plus a routing classifier, improves parameter efficiency and reduces forgetting when expanding LLMs to new languages.
-
Boosting Data Utilization for Multilingual Dense Retrieval
A three-stage data-utilization pipeline for multilingual dense retrieval, combining ensemble hard-negative mining, LLM-based filtering/generation, and monolingual topic-diverse mini-batches, improves MIRACL nDCG@10 by...
-
QUST_NLP at SemEval-2025 Task 7: A Three-Stage Retrieval Framework for Monolingual and Crosslingual Fact-Checked Claim Retrieval
A three-stage ensemble of retrieval models, rerankers, and weighted voting achieves strong multilingual fact-checked claim retrieval results at SemEval-2025 Task 7.
Discussion (0). Continue with ORCID to comment.