New Research Explores How to Boost Large Language Models’ Multilingual Performance – slator.com
In a February 20, 2025 paper, researchers Danni Liu and Jan Niehues from the Karlsruhe Institute of Technology proposed a way to improve how large language models (LLMs) perform across different languages. They explained that LLMs like Llama 3 and Qwen 2.5, show strong performance in tasks like machine translation (MT) but often struggle with […]
Which Parts of a Prompt Should Be Translated to Improve Large Language Models? – slator.com
On February 13, 2025, researchers Itai Mondshine, Tzuf Paz-Argaman, and Reut Tsarfaty from Bar-Ilan University suggested that translating only specific components of a prompt can improve the performance of multilingual large language models (LLMs) across various natural language processing (NLP) tasks. This research builds on prior work by Google, Alibaba, the Pune Institute of Computer […]
Appen Sees ‘Significant Experimentation’ in Large Language Model Market – slator.com
Slator is the leading source of research and market intelligence for translation, localization, interpreting, and language AI. Slator’s Advisory practice is a trusted partner to clients looking for M&A services and independent analysis. Slator has offices in Zurich (HQ) and London, and Analysts based in Asia, Europe, and the US. Source link
Xiaomi’s Training Recipe for Better Multilingual AI Translation – slator.com
In a February 7, 2025 paper, researchers from Chinese tech company Xiaomi benchmarked the capabilities of open-source large language models (LLMs) with under ten billion parameters for multilingual machine translation (MT) tasks. They proposed the “best data recipe” to enhance AI translation performance. The researchers explained that open-source LLMs have shown improvements in multilingual capabilities, […]
Research Pits Traditional Machine Translation Against LLM-Powered AI Translation – slator.com
As large language models (LLMs) continue to transform translation workflows, a new study underscores the ongoing importance of conventional, domain-specific machine translation (MT) models. While recognizing the impact of LLMs on translation processes, the researchers emphasize the need for careful evaluation of workflows to ensure optimal outcomes. Previous research has shown that MT systems often […]
The Most Popular Language Industry Stories of 2024 – slator.com
As 2024 comes to a close, it is time to reflect on the most popular stories, trends, innovations, and themes that made the Slator headlines throughout the year, highlighting key developments in the language industry. Here is a selection of stories that attracted the most attention and engagement from our readers around the world. Will […]
Top Language Industry Quotes of 2024 – slator.com
From the boardrooms of Silicon Valley to the bustling hubs of Asia, language industry leaders recognized the transformative role of AI in translation, interpreting, and multilingual content generation in 2024, particularly the versatility of large language models (LLMs) and the fundamental shift in how laypeople can now interact with language itself. The AI adoption and […]
How Apple Wants to Fix Hallucinations in AI Translation – slator.com
In a January 28, 2025, paper Rajen Chatterjee and Sarthak Garg from Apple, along with Zilu Tang from Boston University, presented a framework for mitigating translation hallucinations in large language models (LLMs). According to the researchers, “this is among the first works to demonstrate how to mitigate translation hallucination in LLMs.” They explained that LLM-based […]