Introduction
Since we published CIOL AI Voices, CIOL Council and delegates at our webinars and Conference days have underlined the importance of enabling linguists to keep abreast of developments in AI with a regularly refreshed resource hub.
Here are some useful recent resources for linguists, which we will add to and update regularly, alongside future CIOL Voices pieces.
Recent AI News & Articles for Linguists
Experts Weigh In on DeepSeek AI Translation Quality
Chinese AI company DeepSeek launched two open-source large language models in January 2025 - DeepSeek-R1 and DeepSeek-V3 - generating significant attention for their ChatGPT-level performance at lower costs. Based in Hangzhou, the company achieved this through efficient training methods including improved Mixture of Experts (MoE) technique, allowing the models to be trained on lower-end hardware while maintaining high performance.
User feedback indicates strong translation capabilities across multiple languages, with particular excellence in Chinese-English translation. Users reported superior performance compared to other models in languages including Serbian, Spanish, Turkish, Czech, Hungarian, Punjabi, and Malayalam. Business adoption has proven cost-effective, with one CEO reporting 50x cost reduction compared to Google Translate, though some users still prefer alternatives like Claude or Copilot.
SAFE AI founding members join SlatorPod to discuss AI and Interpreting
Katharine Allen, Director of Language Industry Learning at Boostlingo, and Dr. Bill Rivers, Principal at WP Rivers & Associates, join SlatorPod to discuss the challenges and opportunities AI brings to interpreting. Both are founding members of the Interpreting SAFE AI Task Force, which aims to guide the responsible use of AI in language services (for more on SAFE AI see below).
Allen describes AI as a double-edged sword — capable of expanding multilingual access but limited in its ability to handle the nuanced human dialogue essential in fields like healthcare. She emphasizes the ongoing shift toward a hybrid model, where human interpreters collaborate with AI tools.
Microsoft Future of Work Report 2024
Microsoft's 2024 Future of Work Report reveals a critical inflection point in AI's impact on language work, showing how generative AI is reshaping professional workflows while highlighting persistent challenges. The report suggests that for language professionals, the shift is twofold: from pure translation/interpretation to higher-order skills like cultural adaptation and quality assurance; and from viewing AI as a replacement to seeing it as a collaborative tool requiring expertise in prompt engineering and output verification. The report documents real productivity gains in document creation and email processing, while emphasising that human expertise remains essential for cultural nuance and context.
However, the report flags serious concerns about AI's current limitations with low-resource languages, potentially excluding billions from the digital economy. When AI systems do handle these languages, they typically deliver lower quality outputs at higher costs with less cultural relevance. While initiatives like Masakhane and ELLORA are working to address these gaps through community-driven datasets and technological innovation, the report emphasises that success requires both technological advancement and deep engagement with local linguistic and cultural knowledge, moving away from the 'extractive' data practices of the past.
In 2023 Microsoft flagged (on p36) the important concept of an increased risk of “moral crumple zones". It pointed out that studies of past 'automations' teach us that when new technologies are poorly integrated within work/organisational arrangements, workers can unfairly take the blame when a crisis or disaster unfolds. This can occur when automated systems only hand over to humans at the worst possible moments, when it is very difficult to either spot, fix or correct the problem before it is too late. A very real concern for linguists.
This could be compounded by 'monitoring and takeover challenges' (set out on p35) where jobs increasingly require individuals to oversee what intelligent systems are doing and intervene when needed. However studies reveal potential challenges. Monitoring requires vigilance, but people struggle to maintain attention on monitoring tasks for more than half an hour, even if they are highly motivated. Again a problem for linguists in post editing or AI assisted interpreting contexts.
These will likely be challenges linguists will face, alongside the many possibilities and opportunities that these reports calls out.
Google Finds ‘Refusal to Translate’ Most Common Form of LLM Verbosity
Researchers from Google have identified 'verbosity' as a key challenge in evaluating large language models (LLMs) for machine translation (MT). Verbosity refers to instances where LLMs provide the reasoning behind their translation choices, offer multiple translations, or refuse to translate certain content. This behavior contrasts with traditional MT systems, which are optimized for producing a single translation.
The study found that verbosity varies across models, with Google's Gemini-1.5-Pro being the most verbose and OpenAI’s GPT-4 among the least verbose. The most common form of verbosity was refusal to translate, notably seen in Claude-3.5.
A major concern is that current translation evaluation frameworks do not account for verbose behaviors, often penalizing models that exhibit verbosity, which can distort performance rankings. The researchers suggest two possible solutions: modifying LLM outputs to fit standardised evaluation metrics or updating evaluation frameworks to better accommodate varied responses. However, these solutions may not fully address verbosity-induced errors or reward 'useful' verbosity, highlighting the need for more nuanced evaluation methods.
AI Pioneer Thinks AI Is Dumber Than a Cat
Yann LeCun helped give birth to today’s artificial-intelligence boom. But he thinks many experts are exaggerating its power and peril, and he wants people to know it.
“We are used to the idea that people or entities that can express themselves, or manipulate language, are smart—but that’s not true,” says LeCun.
“You can manipulate language and not be smart, and that’s basically what LLMs are demonstrating.”
Today’s models are really just predicting the next word in a text, he says. But they’re so good at this that they fool us. And because of their enormous memory capacity, they can seem to be reasoning, when in fact they’re merely regurgitating information they’ve already been trained on.
Google Aligns LLM Translation with Human Translation Processes
Google researchers have developed a new multi-step process to improve translation quality in large language models (LLMs) by mimicking human translation workflows. This approach involves four stages: pre-translation research, drafting, refining, and proofreading and aims to enhance accuracy and context in translations. Tested across ten languages, this method outperformed traditional translation techniques, especially in translations where context is crucial.
How the Media Covers the 'AI vs Translators' Debate
Slator reports that translation is one of the most-referenced professions in 2024's media coverage of AI’s potential impact. Generally, according to Slator, media opinions on AI and translation can be grouped into one of four categories: "Humans Are Still Superior (For Now)" with c25% of articles, "Translators Are In Danger", accounting for c40% of articles, "AI + Humans = Optimal Translation" was the message of around 20% of articles and finally c15% of articles talked about translation but without mentioning translators at all. A mixed picture at best.