Leveraging LLMs for Synthesizing Training Data Across Many Languages …
Nov 10, 2023 · Using SWIM-IR, we explore synthetic fine-tuning of multilingual dense retrieval models and evaluate them robustly on three retrieval benchmarks: XOR-Retrieve (cross …
Using SWIM-IR, we explore synthetic ne- tuning of multilingual dense retrieval models and evaluate them robustly on three retrieval benchmarks: XOR-Retrieve (cross-lingual), MIRACL …
Leveraging LLMs for Synthesizing Training Data Across Many Languages …
Using JUMP-IR, we explore synthetic fine-tuning of multilingual dense retrieval models and evaluate them robustly on three retrieval benchmarks: XOR-Retrieve (cross-lingual), XTREME …
How Is Language Intelligence Evolving? A Multi-Dimensional …
6 days ago · It connects foundations, architectures, training and scaling, applications, and governance within a single framework. At the technical level, we review how LLMs have …
Transformative role of large language models in education and ...
Dec 5, 2025 · This systematic review analyzes 85 peer-reviewed studies to conceptualize the transformative role of Large Language Models (LLMs) in educational ecosystems. Grounded …
LLMs vs SLMs: A Complete Guide to Choosing the Right Model
2 days ago · Compare LLMs vs SLMs, explore real use cases, and learn how to choose the right language model for your organization’s AI strategy.
Leveraging LLMs for Synthesizing Training Data Across Many Languages …
Jan 1, 2024 · To address this problem, we investigate zero-shot cross-lingual entity linking, in which we assume no bilingual lexical resources are available in the source low-resource …
List of large language models - Wikipedia
A large language model (LLM) is a type of machine learning model designed for natural language processing tasks such as language generation. LLMs are language models with many …
Leveraging LLMs for Synthesizing Training Data Across Many Languages …
Using SWIM-IR, we explore synthetic fine-tuning of multilingual dense retrieval models and evaluate them robustly on three retrieval benchmarks: XOR-Retrieve (cross-lingual), XTREME …
Abstract a available across multiple languages. Synthetic training data generation is promising (e.g., InPars or Promptagator), but
Leveraging LLMs for Synthesizing Training Data Across Many Languages …
6 days ago · Abstract There has been limited success for dense retrieval models in multilingual retrieval, due to uneven and scarce training data available across multiple languages. …
2Throughout the paper, we use “multilingual retrieval” to col-lectively denote both cross-language, i.e., cross-lingual and within language, i.e., monolingual retrieval tasks.
Large Language Models in Healthcare and Medical Applications: …
Jun 10, 2025 · A central challenge in deploying LLMs in healthcare is the heterogeneity of data across languages, demographic groups, healthcare systems, and data quality. Healthcare …
Leveraging LLMs for Synthesizing Training Data Across Many Languages …
Large Language Models as Foundations for Next-Gen Dense Retrieval: A Comprehensive Empirical Assessment (2024.emnlp-main) Copied to clipboard Kun Luo, Minghao Qin, Zheng …
Leveraging LLMs for Synthesizing Training Data Across Many Languages …
This work presents a novel modular dense retrieval model that learns from the rich data of a single high-resource language and effectively zero-shot transfers to a wide array of languages, …